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• Machine Learning
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Predictive analytics Machine learning techniques
For such cases, machine learning
techniques emulate human cognition
and learn from training examples to
predict future events.
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Predictive analytics Machine learning techniques
A brief discussion of some of these
methods used commonly for predictive
analytics is provided below. A detailed
study of machine learning can be found in
Mitchell (1997).
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Decentralized Autonomous Corporation - Machine learning layer
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This layer runs the Artificial Intelligence
algorithm that the DAC relies on to
detect patterns in real-world data and
model it without human intervention.
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Machine learning
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Machine learning, a branch of Artificial
Intelligence, concerns the construction
and study of systems that can learn
from data. For example, a machine
learning system could be trained on
email messages to learn to distinguish
between spam and non-spam
messages. After learning, it can then be
used to classify new email messages
into spam and non-spam folders.
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Machine learning
The core of machine learning deals with
representation and generalization.
Representation of data instances and
functions evaluated on these instances are
part of all machine learning systems.
Generalization is the property that the system
will perform well on unseen data instances;
the conditions under which this can be
guaranteed are a key object of study in the
subfield of computational learning theory.
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Machine learning
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There is a wide variety of machine
learning tasks and successful
applications. Optical character
recognition, in which printed
characters are recognized
automatically based on previous
examples, is a classic example of
machine learning.
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Machine learning - Definition
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In 1959, Arthur Samuel defined machine
learning as a "Field of study that gives
computers the ability to learn without being
explicitly programmed".
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Machine learning - Definition
This definition is notable for its
defining machine learning in
fundamentally operational rather than
cognitive terms, thus following Alan
Turing's proposal in Turing's paper
"Computing Machinery and
Intelligence" that the question "Can
machines think?" be replaced with
the question "Can machines do what
we (as thinking entities) can do?"
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Machine learning - Generalization
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A core objective of a learner is to
generalize from its experience
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Machine learning - Machine learning and data mining
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These two terms are commonly confused,
as they often employ the same methods
and overlap significantly. They can be
roughly defined as follows:
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Machine learning - Machine learning and data mining
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Machine learning focuses on prediction, based
on known properties learned from the training
data.
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Machine learning - Machine learning and data mining
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Data mining focuses on the discovery of
(previously) unknown properties in the
data. This is the analysis step of
Knowledge Discovery in Databases.
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Machine learning - Machine learning and data mining
Much of the confusion between these two
research communities (which do often have
separate conferences and separate journals,
ECML PKDD being a major exception)
comes from the basic assumptions they work
with: in machine learning, performance is
usually evaluated with respect to the ability to
reproduce known knowledge, while in
Knowledge Discovery and Data Mining (KDD)
the key task is the discovery of previously
unknown knowledge
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Machine learning - Human interaction
Some machine learning systems
attempt to eliminate the need for
human intuition in data analysis,
while others adopt a collaborative
approach between human and
machine. Human intuition cannot,
however, be entirely eliminated, since
the system's designer must specify
how the data is to be represented and
what mechanisms will be used to
search for a characterization of the
data.
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Machine learning - Algorithm types
Machine learning algorithms can be
organized into a taxonomy based on the
desired outcome of the algorithm or the
type of input available during training the
machine.
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Machine learning - Algorithm types
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Supervised learning algorithms are trained
on labelled examples, i.e., input where the
desired output is known. The supervised
learning algorithm attempts to generalise a
function or mapping from inputs to outputs
which can then be used to speculatively
generate an output for previously unseen
inputs.
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Machine learning - Algorithm types
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Unsupervised learning algorithms
operate on unlabelled examples, i.e.,
input where the desired output is
unknown. Here the objective is to
discover structure in the data (e.g.
through a cluster analysis), not to
generalise a mapping from inputs to
outputs.
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Machine learning - Algorithm types
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Semi-supervised learning combines
both labeled and unlabelled examples
to generate an appropriate function or
classifier.
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Machine learning - Algorithm types
Transduction, or transductive inference,
tries to predict new outputs on specific and
fixed (test) cases from observed, specific
(training) cases.
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Machine learning - Algorithm types
Reinforcement learning is concerned
with how intelligent agents ought to act
in an environment to maximise some
notion of reward. The agent executes
actions which cause the observable state
of the environment to change. Through a
sequence of actions, the agent attempts
to gather knowledge about how the
environment responds to its actions, and
attempts to synthesise a sequence of
actions that maximises a cumulative
reward.
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Machine learning - Algorithm types
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Learning to learn learns its own inductive bias
based on previous experience.
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Machine learning - Algorithm types
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Developmental learning, elaborated for
Robot learning, generates its own
sequences (also called curriculum) of
learning situations to cumulatively acquire
repertoires of novel skills through
autonomous self-exploration and social
interaction with human teachers, and
using guidance mechanisms such as
active learning, maturation, motor
synergies, and imitation.
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Machine learning - Theory
The computational analysis of machine
learning algorithms and their performance
is a branch of theoretical computer
science known as computational learning
theory. Because training sets are finite and
the future is uncertain, learning theory
usually does not yield guarantees of the
performance of algorithms. Instead,
probabilistic bounds on the performance
are quite common.
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Machine learning - Theory
In addition to performance bounds,
computational learning theorists study the
time complexity and feasibility of learning. In
computational learning theory, a computation
is considered feasible if it can be done in
polynomial time. There are two kinds of time
complexity results. Positive results show that
a certain class of functions can be learned in
polynomial time. Negative results show that
certain classes cannot be learned in
polynomial time.
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Machine learning - Theory
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There are many similarities between
machine learning theory and statistical
inference, although they use different
terms.
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Machine learning - Decision tree learning
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Decision tree learning uses a decision
tree as a predictive model which maps
observations about an item to
conclusions about the item's target
value.
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Machine learning - Association rule learning
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Association rule learning
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Machine learning - Association rule learning
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Association rule learning is a method for
discovering interesting relations between
variables in large databases.
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Machine learning - Artificial neural networks
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artificial neural network
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Machine learning - Artificial neural networks
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An artificial neural network (ANN) learning
algorithm, usually called "neural network"
(NN), is a learning algorithm that is
inspired by the structure and functional
aspects of biological neural networks
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Machine learning - Inductive logic programming
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Inductive logic programming
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Machine learning - Inductive logic programming
Inductive logic programming (ILP) is an
approach to rule learning using logic
programming as a uniform representation
for examples, background knowledge, and
hypotheses. Given an encoding of the
known background knowledge and a set of
examples represented as a logical
database of facts, an ILP system will
derive a hypothesized logic program which
entails all the positive and none of the
negative examples.
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Machine learning - Support vector machines
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Support vector
machines
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Machine learning - Support vector machines
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Support vector machines (SVMs) are a set
of related supervised learning methods
used for classification and regression.
Given a set of training examples, each
marked as belonging to one of two
categories, an SVM training algorithm
builds a model that predicts whether a new
example falls into one category or the
other.
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Machine learning - Clustering
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Cluster analysis is the assignment of a
set of observations into subsets
(called clusters) so that observations
within the same cluster are similar
according to some predesignated
criterion or criteria, while
observations drawn from different
clusters are dissimilar
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Machine learning - Bayesian networks
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A Bayesian network, belief network or
directed acyclic graphical model is a
probabilistic graphical model that
represents a set of random variables
and their conditional independencies
via a directed acyclic graph (DAG)
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Machine learning - Reinforcement learning
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Reinforcement learning is concerned with
how an agent ought to take actions in an
environment so as to maximize some notion
of long-term reward. Reinforcement learning
algorithms attempt to find a policy that maps
states of the world to the actions the agent
ought to take in those states. Reinforcement
learning differs from the supervised learning
problem in that correct input/output pairs are
never presented, nor sub-optimal actions
explicitly corrected.
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Machine learning - Representation learning
Several learning algorithms, mostly
unsupervised learning algorithms, aim
at discovering better representations of
the inputs provided during training
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Machine learning - Similarity and metric learning
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In this problem, the learning machine
is given pairs of examples that are
considered similar and pairs of less
similar objects. It then needs to learn
a similarity function (or a distance
metric function) that can predict if
new objects are similar. It is
sometimes used in Recommendation
systems.
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Machine learning - Sparse Dictionary Learning
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In this method, a datum is represented
as a linear combination of basis
functions, and the coefficients are
assumed to be sparse. Let x be a ddimensional datum, D be a d by n
matrix, where each column of D
represents a basis function. r is the
coefficient to represent x using D.
Mathematically, sparse dictionary
learning means the following where r is
sparse. Generally speaking, n is
assumed to be larger than d to allow the
freedom for a sparse representation.
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Machine learning - Sparse Dictionary Learning
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Sparse dictionary learning has
been applied in several contexts
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Machine learning - Applications
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Applications for machine
learning include:
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Machine learning - Applications
In 2006, the online movie company Netflix
held the first "Netflix Prize" competition to find
a program to better predict user preferences
and improve the accuracy on its existing
Cinematch movie recommendation algorithm
by at least 10%. A joint team made up of
researchers from AT&T Labs-Research in
collaboration with the teams Big Chaos and
Pragmatic Theory built an ensemble model to
win the Grand Prize in 2009 for $1 million.
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Machine learning - Applications
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In 2010 The Wall Street Journal wrote
about a money management firm
Rebellion Research's use of machine
learning to predict economic
movements, the article talks about
Rebellion Research's prediction of the
financial crisis and economic recovery.
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Machine learning - Software
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Ayasdi, Angoss KnowledgeSTUDIO, Apache
Mahout, Gesture Recognition Toolkit, IBM
SPSS Modeler, KNIME, KXEN Modeler,
LIONsolver, MATLAB, mlpy, MCMLL,
OpenCV, dlib, Oracle Data Mining, Orange,
Python scikit-learn, R, RapidMiner, Salford
Predictive Modeler, SAS Enterprise Miner,
Shogun toolbox, STATISTICA Data Miner,
and Weka are software suites containing a
variety of machine learning algorithms.
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Machine learning - Journals and conferences
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Journal of Machine
Learning Research
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Machine learning - Journals and conferences
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Neural Computation (journal)
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Machine learning - Journals and conferences
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Journal of Intelligent
Systems(journal)
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Machine learning - Journals and conferences
Neural Information
Processing Systems (NIPS)
(conference)
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Machine learning - Further reading
Mehryar Mohri, Afshin
Rostamizadeh, Ameet Talwalkar
(2012). Foundations of Machine
Learning, The MIT Press. ISBN
9780262018258.
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Machine learning - Further reading
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Ian H. Witten and Eibe Frank (2011). Data
Mining: Practical machine learning tools
and techniques Morgan Kaufmann,
664pp., ISBN 978-0123748560.
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Machine learning - Further reading
Sergios Theodoridis, Konstantinos
Koutroumbas (2009) "Pattern
Recognition", 4th Edition, Academic Press,
ISBN 978-1-59749-272-0.
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Machine learning - Further reading
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Mierswa, Ingo and Wurst, Michael and
Klinkenberg, Ralf and Scholz, Martin
and Euler, Timm: YALE: Rapid
Prototyping for Complex Data Mining
Tasks, in Proceedings of the 12th ACM
SIGKDD International Conference on
Knowledge Discovery and Data Mining
(KDD-06), 2006.
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Machine learning - Further reading
Bing Liu (2007), Web Data Mining:
Exploring Hyperlinks, Contents and Usage
Data. Springer, ISBN 3-540-37881-2
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Machine learning - Further reading
Huang T.-M., Kecman V., Kopriva I.
(2006), Kernel Based Algorithms for
Mining Huge Data Sets, Supervised,
Semi-supervised, and Unsupervised
Learning, Springer-Verlag, Berlin,
Heidelberg, 260 pp. 96 illus., Hardcover,
ISBN 3-540-31681-7.
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Machine learning - Further reading
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Ethem Alpaydın (2004) Introduction to
Machine Learning (Adaptive
Computation and Machine Learning),
MIT Press, ISBN 0-262-01211-1
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Machine learning - Further reading
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MacKay, D.J.C. (2003). Information
Theory, Inference, and Learning
Algorithms, Cambridge University
Press. ISBN 0-521-64298-1.
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Machine learning - Further reading
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KECMAN Vojislav (2001), Learning
and Soft Computing, Support Vector
Machines, Neural Networks and
Fuzzy Logic Models, The MIT Press,
Cambridge, MA, 608 pp., 268 illus.,
ISBN 0-262-11255-8.
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Machine learning - Further reading
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Richard O. Duda, Peter E. Hart, David G.
Stork (2001) Pattern classification (2nd
edition), Wiley, New York, ISBN 0-47105669-3.
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Machine learning - Further reading
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Bishop, C.M. (1995). Neural Networks for
Pattern Recognition, Oxford University
Press. ISBN 0-19-853864-2.
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Machine learning - Further reading
Ryszard S. Michalski, George Tecuci
(1994), Machine Learning: A Multistrategy
Approach, Volume IV, Morgan Kaufmann,
ISBN 1-55860-251-8.
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Machine learning - Further reading
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Sholom Weiss and Casimir Kulikowski
(1991). Computer Systems That Learn,
Morgan Kaufmann. ISBN 1-55860-065-5.
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Machine learning - Further reading
Yves Kodratoff, Ryszard S. Michalski
(1990), Machine Learning: An Artificial
Intelligence Approach, Volume III, Morgan
Kaufmann, ISBN 1-55860-119-8.
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Machine learning - Further reading
Ryszard S. Michalski, Jaime G.
Carbonell, Tom M. Mitchell (1986),
Machine Learning: An Artificial
Intelligence Approach, Volume II,
Morgan Kaufmann, ISBN 0-934613-00-1.
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Machine learning - Further reading
Ryszard S. Michalski, Jaime G.
Carbonell, Tom M. Mitchell (1983),
Machine Learning: An Artificial
Intelligence Approach, Tioga
Publishing Company, ISBN 0-93538205-4.
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Machine learning - Further reading
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Ray Solomonoff, An Inductive Inference
Machine, IRE Convention Record, Section
on Information Theory, Part 2, pp., 56-62,
1957.
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Machine learning - Further reading
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Ray Solomonoff, "An Inductive
Inference Machine" A privately
circulated report from the 1956
Dartmouth Summer Research
Conference on AI.
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Natural language processing - NLP using machine learning
The paradigm of machine learning is
different from that of most prior attempts at
language processing
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Natural language processing - NLP using machine learning
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Many different classes of machine learning
algorithms have been applied to NLP tasks
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Natural language processing - NLP using machine learning
Systems based on machine-learning
algorithms have many advantages over handproduced rules:
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Natural language processing - NLP using machine learning
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The learning procedures used during
machine learning automatically focus
on the most common cases, whereas
when writing rules by hand it is often
not obvious at all where the effort
should be directed.
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Natural language processing - NLP using machine learning
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Automatic learning procedures can make use
of statistical inference algorithms to produce
models that are robust to unfamiliar input
(e.g. containing words or structures that have
not been seen before) and to erroneous input
(e.g. with misspelled words or words
accidentally omitted). Generally, handling
such input gracefully with hand-written rules
— or more generally, creating systems of
hand-written rules that make soft decisions —
is extremely difficult, error-prone and timeconsuming.
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Natural language processing - NLP using machine learning
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Systems based on automatically learning
the rules can be made more accurate
simply by supplying more input data
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Natural language processing - NLP using machine learning
The subfield of NLP devoted to learning
approaches is known as Natural Language
Learning (NLL) and its conference CoNLL
and peak body SIGNLL are sponsored by
ACL, recognizing also their links with
Computational Linguistics and Language
Acquisition. When the aims of computational
language learning research is to understand
more about human language acquisition, or
psycholinguistics, NLL overlaps into the
related field of Computational
Psycholinguistics.
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Functional decomposition - Machine learning
In practical scientific applications, it is
almost never possible to achieve perfect
functional decomposition because of the
incredible complexity of the systems under
study. This complexity is manifested in the
presence of "noise," which is just a
designation for all the unwanted and
untraceable influences on our
observations.
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Functional decomposition - Machine learning
However, while perfect functional
decomposition is usually impossible, the
spirit lives on in a large number of
statistical methods that are equipped to
deal with noisy systems
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Functional decomposition - Machine learning
As an example, Bayesian network
methods attempt to decompose a joint
distribution along its causal fault lines, thus
"cutting nature at its seams"
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Data compression - Machine learning
There is a close connection between
machine learning and compression: a
system that predicts the posterior
probabilities of a sequence given its
entire history can be used for optimal
data compression (by using arithmetic
coding on the output distribution) while
an optimal compressor can be used for
prediction (by finding the symbol that
compresses best, given the previous
history). This equivalence has been used
as justification for data compression as a
benchmark for general intelligence.
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Self-modifying code - Self-referential machine learning systems
Traditional machine learning systems
have a fixed, pre-programmed learning
algorithm to adjust their parameters.
However, since the 1980s Jürgen
Schmidhuber has published several selfmodifying systems with the ability to
change their own learning algorithm. They
avoid the danger of catastrophic selfrewrites by making sure that selfmodifications will survive only if they are
useful according to a user-given fitness
function|fitness, error function|error or
reward function|reward function.
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Andrew Ng - Machine learning research
In 2011, Ng founded the Google Brain
project at Google, which developed very
large scale artificial neural networks using
Google's distributed compute
infrastructure.
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Andrew Ng - Machine learning research
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Among its notable results was a neural
network trained using deep learning
algorithms on 16,000 CPU cores, that
learned to recognize higher-level
concepts, such as cats, after watching
only YouTube videos, and without ever
having been told what a cat is.
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Andrew Ng - Machine learning research
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The project's technology is currently
also used in the Android (Operating
System)|Android Operating System's
speech recognition system.
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Pattern recognition - Classification (machine learning)|Classification algorithms
(supervised learning|supervised algorithms predicting categorical data|categorical labels)
Parametric:Assuming known
distributional shape of feature
distributions per class, such as the
Gaussian distribution|Gaussian shape.
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Pattern recognition - Classification (machine learning)|Classification algorithms
(supervised learning|supervised algorithms predicting categorical data|categorical labels)
*Maximum entropy classifier (aka
logistic regression, multinomial logistic
regression): Note that logistic
regression is an algorithm for
classification, despite its name. (The
name comes from the fact that logistic
regression uses an extension of a
linear regression model to model the
probability of an input being in a
particular class.)
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Pattern recognition - Classification (machine learning)|Classification algorithms
(supervised learning|supervised algorithms predicting categorical data|categorical labels)
1
Nonparametric:No distributional assumption
regarding shape of feature distributions per
class.
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Pattern recognition - Classification (machine learning)|Classification algorithms
(supervised learning|supervised algorithms predicting categorical data|categorical labels)
*Variable kernel density
estimation#Use for statistical
classification|Kernel estimation and
K-nearest-neighbor algorithms
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Pattern recognition - Classification (machine learning)|Classification algorithms
(supervised learning|supervised algorithms predicting categorical data|categorical labels)
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*Neural networks (multi-layer
perceptrons)
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Pattern recognition - Classification (machine learning)|Classification algorithms
(supervised learning|supervised algorithms predicting categorical data|categorical labels)
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*Support vector machines
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List of machine learning algorithms - Supervised learning
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* Artificial neural network
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List of machine learning algorithms - Supervised learning
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** Spiking neural
networks
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List of machine learning algorithms - Supervised learning
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* Inductive logic
programming
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List of machine learning algorithms - Supervised learning
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* Gaussian process
regression
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List of machine learning algorithms - Supervised learning
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* Group method of data handling
(GMDH)
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List of machine learning algorithms - Supervised learning
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* Learning Automata
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List of machine learning algorithms - Supervised learning
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* Learning Vector
Quantization
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List of machine learning algorithms - Supervised learning
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* Minimum message length
(decision trees, decision
graphs, etc.)
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List of machine learning algorithms - Supervised learning
1
* Ripple down rules, a
knowledge
acquisition
methodology
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List of machine learning algorithms - Supervised learning
1
* Subsymbolic machine learning
algorithms
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List of machine learning algorithms - Supervised learning
1
* Support vector machines
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List of machine learning algorithms - Supervised learning
* Information Fuzzy
Networks|Information
fuzzy networks (IFN)
1
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List of machine learning algorithms - Statistical classification
1
** Multinomial logistic
regression
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List of machine learning algorithms - Statistical classification
1
** Support vector
machines
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List of machine learning algorithms - Unsupervised learning
1
* Radial basis function
network
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List of machine learning algorithms - Unsupervised learning
* Vector
Quantization
1
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List of machine learning algorithms - Association rule learning
1
* Association_rule_learning#FPgrowth_algorithm|FP-growth
algorithm
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List of machine learning algorithms - Hierarchical clustering
1
* Conceptual clustering
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List of machine learning algorithms - Deep learning
1
* Deep Convolutional neural
networks
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Identity resolution - Machine learning
1
Higher accuracy can often be
achieved by using various other
machine learning techniques,
including a single-layer
perceptron.Wilson, D
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Bootstrapping - Artificial intelligence and machine learning
1
Bootstrapping is a technique used to
iteratively improve a classifier
(machine learning)|classifier's
performance. Seed AI is a
hypothesized type of strong Artificial
Intelligence capable of
recursion|recursive selfimprovement. Having improved itself,
it would become better at improving
itself, potentially leading to an
exponential increase in intelligence.
No such AI is known to exist, but it
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Bootstrapping - Artificial intelligence and machine learning
Seed AI is a significant part of some
theories about the technological
singularity: proponents believe that the
development of seed AI will rapidly yield
ever-smarter intelligence (via
bootstrapping) and thus a new era.
1
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Monte Carlo Machine Learning Library
The 'Monte Carlo Machine Learning
Library (MCMLL)' is an open source C++
template library which already relies on
some C++0x specs. MCMLL is licensed
under the GNU GPL. It is developed under
the 64 bit Linux OS. MCMLL should be
usable on other platforms as well, since it
is based on International Organization for
Standardization|ISO C++.
1
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Monte Carlo Machine Learning Library
1
The philosophy behind MCMLL is to
have a broad range support for Monte
Carlo methods to implement machine
learning applications. Since Monte
Carlo methods are inherently Parallel
algorithm|parallelizable, the goal is
to provide multi-threaded
implementations of the most
important methods.
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Monte Carlo Machine Learning Library - Overview
1
* complete framework for vector
and matrix computations
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Monte Carlo Machine Learning Library - Overview
1
* multi-threaded support
for generic Evolutionary
algorithms (EA)
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Monte Carlo Machine Learning Library - Overview
1
* support for generic Sequential
Monte Carlo methods ('Particle
Filtering').
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Monte Carlo Machine Learning Library - Overview
1
Example applications
include:
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Monte Carlo Machine Learning Library - Overview
* support for
learning Artificial
Neural Networks
(ANN) using EA's
1
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Monte Carlo Machine Learning Library - Overview
1
* example programs for
Sequential Monte Carlo
methods ('Particle
Filtering')
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Monte Carlo Machine Learning Library - Overview
1
* a benchmark suite for
testing and
implementing
Evolutionary
Algorithms.
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Monte Carlo Machine Learning Library - Supported Evolutionary Algorithms
DOI=10.1109/TEVC.2009.2014613
http://dx.doi.org/10.1109/TEVC.2009.2014
613 without history, R2DE,Onay Urfalioglu
and Orhan Arikan, Randomized and Rank
Based Differential Evolution, Machine
Learning and Applications, Fourth
International Conference on, vol
1
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Monte Carlo Machine Learning Library - Supported Evolutionary Algorithms
* Covariance Matrix
Adaptation Evolution
Strategies (CMA-ES)
1
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Monte Carlo Machine Learning Library - Supported Sequential Monte Carlo Methods
For particle filtering, the Particle
filter|Sequential Importance Resampling
(SIR) method is supported. To create an
SMC application based on MCMLL, one
has to define an observation distribution, a
transition distribution and optionally an
importance distribution to be used in the
SIR operator.
1
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Online machine learning
1
Online machine learning is a model of
inductive reasoning|induction that
learns one instance at a time
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Online machine learning
1
Third the algorithm receives the true label
of the instance.Littlestone, Nick; (1988)
Learning Quickly When Irrelevant
Attributes Abound: A New Linear-threshold
Algorithm, Machine Learning 285-318(2),
Kluwer Academic Publishers The third
stage is the most crucial as the algorithm
can use this label feedback to update its
hypothesis for future trials
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Online machine learning
Because on-line learning algorithms
continually receive label feedback, the
algorithms are able to adapt and learn in
difficult situations
1
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Online machine learning
1
Unfortunately, the main difficulty of online learning is also a result of the
requirement for continual label
feedback
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Online machine learning - A prototypical online supervised learning algorithm
1
In the setting of supervised learning,
or learning from examples, we are
interested in learning a function f : X
\to Y, where X is thought of as a space
of inputs and Y as a space of outputs,
that predicts well on instances that are
drawn from a joint probability
distribution p(x,y) on X \times Y
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Online machine learning - A prototypical online supervised learning algorithm
1
In reality, the learner never knows the true
distribution p(x,y) over instances
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Online machine learning - A prototypical online supervised learning algorithm
1
The above paradigm is not well-suited to
the online learning setting though, as it
requires complete a priori knowledge of
the entire training set
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Online machine learning - The algorithm and its interpretations
1
Here we outline a prototypical online
learning algorithm in the supervised
learning setting and we discuss several
interpretations of this algorithm
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Online machine learning - The algorithm and its interpretations
1
where w_1 \gets 0 , \nabla V(\langle w_t,
x_t \rangle, y_t) is the gradient of the loss
for the next data point (x_t, y_t) evaluated
at the current linear functional w_t, and
\gamma_t !-- Bot inserted parameter.
Either remove it; or change its value to .
for the cite to end in a ., as necessary. --ref
name=kushneryinreferences /
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Weka (machine learning)
'Weka' (Waikato Environment for
Knowledge Analysis) is a popular suite of
machine learning software written in Java
(programming language)|Java, developed
at the University of Waikato, New Zealand.
Weka is free software available under the
GNU General Public License.
1
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Weka (machine learning) - Description
1
The original non-Java version of Weka
was a Tcl|TCL/TK front-end to
(mostly third-party) modeling
algorithms implemented in other
programming languages, plus data
preprocessing utilities in C
(programming language)|C, and a
Makefile-based system for running
machine learning experiments
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Weka (machine learning) - Description
* portability, since it is fully
implemented in the Java
programming language and thus runs
on almost any modern computing
platform
1
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Weka (machine learning) - Description
* a comprehensive
collection of data
preprocessing and
modeling techniques
1
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Weka (machine learning) - Description
1
* ease of use due to its
graphical user interfaces
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Weka (machine learning) - Description
1
Weka supports several standard data
mining tasks, more specifically, data
preprocessing, data
clustering|clustering, Statistical
classification|classification,
Regression analysis|regression,
visualization, and feature selection
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Weka (machine learning) - Description
1
Weka's main user interface is the Explorer,
but essentially the same functionality can
be accessed through the componentbased Knowledge Flow interface and from
the command line. There is also the
Experimenter, which allows the systematic
comparison of the predictive performance
of Weka's machine learning algorithms on
a collection of datasets.
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Weka (machine learning) - Description
1
The Explorer interface features several
panels providing access to the main
components of the workbench:
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Weka (machine learning) - Description
1
* The Preprocess panel has facilities for
importing data from a database, a
Comma-separated values|CSV file, etc.,
and for preprocessing this data using a socalled filtering algorithm. These filters can
be used to transform the data (e.g., turning
numeric attributes into discrete ones) and
make it possible to delete instances and
attributes according to specific criteria.
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Weka (machine learning) - Description
1
* The Classify panel enables the user to
apply Statistical
classification|classification and
Regression analysis|regression
algorithms (indiscriminately called
classifiers in Weka) to the resulting
dataset, to estimate the accuracy of the
resulting Predictive
modeling|predictive model, and to
visualize erroneous predictions,
Receiver operating characteristic|ROC
curves, etc., or the model itself (if the
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Weka (machine learning) - Description
1
* The Associate panel provides access
to Association rule learning|association
rule learners that attempt to identify all
important interrelationships between
attributes in the data.
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Weka (machine learning) - Description
1
* The Cluster panel gives access to the
cluster analysis|clustering techniques in
Weka, e.g., the simple k-means algorithm.
There is also an implementation of the
Expectation-maximization
algorithm|expectation maximization
algorithm for learning a mixture of normal
distributions.
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Weka (machine learning) - Description
1
* The Select attributes panel provides
algorithms for identifying the most
predictive attributes in a dataset.
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Weka (machine learning) - Description
1
* The Visualize panel shows a scatter
plot matrix, where individual scatter
plots can be selected and enlarged,
and analyzed further using various
selection operators.
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Weka (machine learning) - History
1
* In 1993, the University of Waikato in
New Zealand started development of
the original version of Weka (which
became a mixture of TCL/TK, C, and
Makefiles).
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Weka (machine learning) - History
1
* In 1997, the decision was made to
redevelop Weka from scratch in Java,
including implementations of modeling
algorithms.
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Weka (machine learning) - History
* In 2006, Pentaho|Pentaho
Corporation acquired an exclusive
licence to use Weka for business
intelligence. It forms the data mining
and predictive analytics component of
the Pentaho business intelligence
suite.
1
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Weka (machine learning) - History
*[
http://sourceforge.net/top/topalltime
.php?type=downloadsoffset=200 Alltime ranking] on Sourceforge.net as of
2011-08-26, 243 (with 2,487,213
downloads)
1
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Machine Learning (journal)
1
'Machine Learning' is a peer-review|peer-reviewed
scientific journal, published since 1986.
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Machine Learning (journal)
1
In 2001, forty editors and members of the
editorial board of Machine Learning
resigned in order to found the Journal of
Machine Learning Research (JMLR),
saying that in the era of the internet, it
was detrimental for researchers to
continue publishing their papers in
expensive journals with pay-access
archives. Instead, they wrote, they
supported the model of JMLR, in which
authors retained copyright over their
papers and archives were freely available
on the internet.
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Journal of Machine Learning Research
1
The 'Journal of Machine Learning
Research' (usually abbreviated
'JMLR'), is a scientific journal
focusing on machine learning, a
subfield of Artificial Intelligence. It
was founded in 2000.
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Journal of Machine Learning Research
1
In 2001, forty editors of Machine Learning
resigned in order to support JMLR, saying
that in the era of the internet, it was
detrimental for researchers to continue
publishing their papers in expensive
journals with pay-access archives
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Journal of Machine Learning Research
1
Print editions of JMLR were published
by MIT Press until 2004, and by
Microtome Publishing thereafter.
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Journal of Machine Learning Research
Since Summer 2007 JMLR is also
publishing
[http://www.jmlr.org/mloss Machine
Learning Open Source Software ].
1
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Boosting (machine learning)
1
Boosting is based on the question posed by
Kearns:Michael Kearns (1988);
[http://www.cis.upenn.edu/~mkearns/papers/
boostnote.pdf Thoughts on Hypothesis
Boosting], Unpublished manuscript (Machine
Learning class project, December 1988) Can
a set of 'weak learners' create a single 'strong
learner'? A weak learner is defined to be a
classifier which is only slightly correlated with
the true classification (it can label examples
better than random guessing)
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Boosting (machine learning)
Schapire's affirmative answer to
Kearns' question has had significant
ramifications in machine learning
and statistics, most notably leading to
the development of boosting.
1
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Boosting (machine learning)
1
When first introduced, the hypothesis
boosting problem simply referred to the
process of turning a weak learner into a
strong learner
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Boosting (machine learning) - Boosting algorithms
While boosting is not algorithmically
constrained, most boosting algorithms
consist of iteratively learning weak
classifiers with respect to a distribution
and adding them to a final strong classifier
1
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Boosting (machine learning) - Boosting algorithms
There are many boosting
algorithms. The original ones,
proposed by Robert Schapire (a
recursive majority gate formulation)
and Yoav Freund (boost by
majorityLlew Mason, Jonathan Baxter,
Peter Bartlett, and Marcus Frean (2000);
Boosting Algorithms as Gradient
Descent, in S
1
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Boosting (machine learning) - Examples of boosting algorithms
The main variation between many
boosting algorithms is their method of
weighting training data points and
hypotheses
1
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Boosting (machine learning) - Criticism
In 2008 Phillip Long (at Google) and
Rocco A. Servedio (Columbia University)
published
[http://www.phillong.info/publications/LS10
_potential.pdf a paper] at the 25th
International Conference for Machine
Learning suggesting that many of these
algorithms are probably flawed. They
conclude that convex potential boosters
cannot withstand random classification
1
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Boosting (machine learning) - Criticism
Servedio (2010); Random
Classification Noise Defeats All
Convex Potential Boosters, Machine
Learning 78(3), pp
1
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Transduction (machine learning)
In logic, statistical
inference, and
supervised learning,
1
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Transduction (machine learning)
'transduction' or
'transductive inference'
is reasoning from
1
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Transduction (machine learning)
induction
(philosophy)|inducti
on is reasoning from
observed training
cases
1
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Transduction (machine learning)
1
to general rules, which are then applied to the
test cases. The distinction is
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Transduction (machine learning)
1
most interesting in cases where the predictions
of the transductive model are
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Transduction (machine learning)
1
not achievable by any inductive model. Note that
this is caused by transductive
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Transduction (machine learning)
1
inference on different test sets producing
mutually inconsistent predictions.
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Transduction (machine learning)
Transduction was
introduced by Vladimir
Vapnik in the 1990s,
motivated by
1
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Transduction (machine learning)
1
his view that transduction is preferable to induction
since, according to him, induction requires
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Transduction (machine learning)
solving a more general
problem (inferring a
function) before solving a
more
1
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Transduction (machine learning)
1
specific problem (computing outputs for new cases):
When solving a problem of
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Transduction (machine learning)
1
An example of learning
which is not inductive
would be in the case of
binary
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Transduction (machine learning)
1
classification, where the inputs tend to cluster in two
groups. A large set of
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Transduction (machine learning)
1
test inputs may help in finding the clusters, thus
providing useful information
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Transduction (machine learning)
1
about the classification labels. The same predictions
would not be obtainable
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Transduction (machine learning)
1
from a model which induces a function based
only on the training cases. Some
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Transduction (machine learning)
people may call this an example of the
closely related semi-supervised learning,
since Vapnik's motivation is quite different.
An example of an algorithm in this
category is the Transductive Support
Vector Machine (TSVM).
1
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Transduction (machine learning)
1
A third possible motivation which leads to
transduction arises through the need
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Transduction (machine learning)
1
to approximate. If exact inference is computationally
prohibitive, one may at
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Transduction (machine learning)
1
least try to make sure that the approximations are
good at the test inputs. In
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Transduction (machine learning)
1
this case, the test inputs
could come from an
arbitrary distribution
(not
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Transduction (machine learning)
1
necessarily related to the distribution of the
training inputs), which wouldn't
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Transduction (machine learning)
1
be allowed in semi-supervised learning. An example
of an algorithm falling in
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Transduction (machine learning) - Example Problem
1
The following example problem
contrasts some of the unique
properties of transduction against
induction.
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Transduction (machine learning) - Example Problem
1
A collection of points is given, such
that some of the points are labeled (A,
B, or C), but most of the points are
unlabeled (?). The goal is to predict
appropriate labels for all of the
unlabeled points.
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Transduction (machine learning) - Example Problem
1
The inductive approach to solving this
problem is to use the labeled points to
train a supervised learning algorithm,
and then have it predict labels for all of
the unlabeled points
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Transduction (machine learning) - Example Problem
1
Transduction has the advantage of being
able to consider all of the points, not just
the labeled points, while performing the
labeling task. In this case, transductive
algorithms would label the unlabeled
points according to the clusters to which
they naturally belong. The points in the
middle, therefore, would most likely be
labeled B, because they are packed very
close to that cluster.
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Transduction (machine learning) - Example Problem
1
An advantage of transduction is that it
may be able to make better
predictions with fewer labeled points,
because it uses the natural breaks
found in the unlabeled points
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Transduction (machine learning) - Transduction Algorithms
Transduction algorithms can be
broadly divided into two categories:
those that seek to assign discrete
labels to unlabeled points, and those
that seek to regress continuous labels
for unlabeled points
1
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Transduction (machine learning) - Partitioning Transduction
Partitioning transduction can be
thought of as top-down transduction. It
is a semi-supervised extension of
partition-based clustering. It is typically
performed as follows:
1
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Transduction (machine learning) - Partitioning Transduction
Of course, any reasonable partitioning
technique could be used with this
algorithm. Max flow min cut partitioning
schemes are very popular for this purpose.
1
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Transduction (machine learning) - Agglomerative Transduction
1
Agglomerative transduction can be
thought of as bottom-up transduction.
It is a semi-supervised extension of
agglomerative clustering. It is
typically performed as follows:
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Transduction (machine learning) - Agglomerative Transduction
1
Compute the pair-wise distances,
D, between all the points.
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Transduction (machine learning) - Agglomerative Transduction
1
Consider each point to be a
cluster of size 1.
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Transduction (machine learning) - Agglomerative Transduction
1
If (a is unlabeled) or
(b is unlabeled) or (a
and b have the same
label)
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Transduction (machine learning) - Agglomerative Transduction
1
Merge the two clusters
that contain a and b.
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Transduction (machine learning) - Agglomerative Transduction
1
Label all points in the merged
cluster with the same label.
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Transduction (machine learning) - Manifold Transduction
1
Manifold-learning-based transduction is still a
very young field of research.
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BodyMedia - Wearable device and machine learning expertise
The BodyMedia informatics group
made available a large anonymised
human physiology data set for the 2004
International Conference on Machine
Learning, running a Machine Learning
Challenge
1
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Learning curve - In machine learning
1
The machine learning curve is useful for
many purposes including comparing
different algorithms, choosing model
parameters during design, adjusting
optimization to improve convergence, and
determining the amount of data used for
training.
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Protein structure prediction - Machine learning
1
Artificial neural network|Neural network
methods use training sets of solved
structures to identify common sequence
motifs associated with particular
arrangements of secondary structures
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Protein structure prediction - Machine learning
Support vector machines have proven
particularly useful for predicting the
locations of turn (biochemistry)|turns,
which are difficult to identify with statistical
methods. The requirement of relatively
small training sets has also been cited as
an advantage to avoid overfitting to
existing structural data.
1
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Protein structure prediction - Machine learning
Extensions of machine learning
techniques attempt to predict more finegrained local properties of proteins, such
as protein backbone|backbone dihedral
angles in unassigned regions. Both SVMs
and neural networks have been applied to
this problem. More recently, real-value
torsion angles can be accurately predicted
by SPINE-X and successfully employed
for ab initio structure prediction.
1
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Predictive Analysis - Machine learning techniques
For such cases, machine learning
techniques emulate human cognition and
learn from training examples to predict
future events.
1
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Artificial intelligence marketing - Machine Learning
1
Machine learning is concerned with
the design and development of
algorithms and techniques that allow
computers to learn.
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Artificial intelligence marketing - Machine Learning
1
As defined above machine learning is one
of the techniques that can be employed to
enable more effective 'behavioral targeting'
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Bootstrap - Artificial intelligence and machine learning
Bootstrapping is a technique used to
iteratively improve a classifier (machine
learning)|classifier's performance. Seed AI
is a hypothesized type of artificial
intelligence capable of recursive selfimprovement. Having improved itself, it
would become better at improving itself,
potentially leading to an exponential
increase in intelligence. No such AI is
known to exist, but it remains an active
field of research.
1
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Academic studies about Wikipedia - Machine learning
1
Automated Semantic data model|semantic
knowledge extraction using machine
learning algorithms is used to extract
machine-processable information at a
relatively low complexity cost. DBpedia
uses structured content extracted from
infoboxes by machine learning algorithms
to create a resource of linked data in a
Semantic Web.
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Concept learning - Machine learning approaches to concept learning
1
In machine learning, algorithms of
exemplar theory are also known as
instance learners or lazy learners.
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Concept learning - Machine learning approaches to concept learning
1
#Data Mining: using historical data to
improve decisions. An example is
looking at medical records and then
applying one's medical knowledge to
make a diagnosis.
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Concept learning - Machine learning approaches to concept learning
1
#Software applications that cannot be
programmed by hand: examples are
autonomous driving and speech
recognition
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Concept learning - Machine learning approaches to concept learning
#Self-customizing programs: an
example is a newsreader that learns a
reader's particular interests and
highlights them when the reader visits
the site.
1
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Concept learning - Machine learning approaches to concept learning
Machine learning has an exciting
future. Some potential advantages
include: learning across full mixedmedia data, learning across multiple
internal databases (including the
Internet and news feeds), learning by
active experimentation, learning
decisions rather than predictions, and
the possibility of programming
languages with embedded learning.
1
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List of algorithms - Machine learning and statistical classification
* Association rule learning: discover
interesting relations between variables, used in
data mining
1
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List of algorithms - Machine learning and statistical classification
1
** Association rule learning#Eclat
algorithm|Eclat algorithm
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List of algorithms - Machine learning and statistical classification
** Association rule
learning#FP-growth
algorithm|FP-growth
algorithm
1
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List of algorithms - Machine learning and statistical classification
1
** One-attribute rule
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List of algorithms - Machine learning and statistical classification
** Association rule
learning#Zero-attribute
rule|Zero-attribute rule
1
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List of algorithms - Machine learning and statistical classification
* Boosting (metaalgorithm): Use many
weak learners to
boost effectiveness
1
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List of algorithms - Machine learning and statistical classification
** BrownBoost:a
boosting algorithm that
may be robust to noisy
datasets
1
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List of algorithms - Machine learning and statistical classification
1
* Bootstrap aggregating (bagging): technique to
improve stability and classification accuracy
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List of algorithms - Machine learning and statistical classification
1
** ID3 algorithm (Iterative Dichotomiser 3): Use
heuristic to generate small decision trees
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List of algorithms - Machine learning and statistical classification
1
* k-nearest neighbors (k-NN): a method for
classifying objects based on closest
training examples in the feature space
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List of algorithms - Machine learning and statistical classification
* Linde–Buzo–Gray algorithm: a vector
quantization algorithm used to derive a good
codebook
1
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List of algorithms - Machine learning and statistical classification
* Locality-sensitive hashing (LSH): a
method of performing probabilistic
dimension reduction of high-dimensional
data
1
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List of algorithms - Machine learning and statistical classification
1
** Backpropagation: A supervised learning
method which requires a teacher that
knows, or can calculate, the desired output
for any given input
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List of algorithms - Machine learning and statistical classification
1
** Hopfield net: a Recurrent neural network in
which all connections are symmetric
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List of algorithms - Machine learning and statistical classification
1
** Perceptron: the simplest kind of feedforward
neural network: a linear classifier.
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List of algorithms - Machine learning and statistical classification
1
** Pulse-coupled neural networks (PCNN):
Neural network|neural models proposed
by modeling a cat's visual cortex and
developed for high-performance
Bionics|biomimetic image processing.
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List of algorithms - Machine learning and statistical classification
1
** Radial basis function network: an
artificial neural network that uses
radial basis functions as activation
functions
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List of algorithms - Machine learning and statistical classification
1
** Self-organizing map: an unsupervised
network that produces a low-dimensional
representation of the input space of the
training samples
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List of algorithms - Machine learning and statistical classification
* Random forest:
classify using many
decision trees
1
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List of algorithms - Machine learning and statistical classification
1
** Q-learning: learn an action-value
function that gives the expected utility
of taking a given action in a given
state and following a fixed policy
thereafter
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List of algorithms - Machine learning and statistical classification
1
* Relevance Vector Machine (RVM): similar to SVM,
but provides probabilistic classification
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List of algorithms - Machine learning and statistical classification
1
* Support Vector Machines (SVM): a set of
methods which divide multidimensional
data by finding a dividing hyperplane with
the maximum margin between the two
sets
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List of algorithms - Machine learning and statistical classification
** Structured SVM: allows training of a
classifier for general structured output labels.
1
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List of algorithms - Machine learning and statistical classification
1
* Winnow algorithm: related to the perceptron, but
uses a multiplicative weight-update scheme
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Torch (machine learning)
1
'Torch' is an open source deep
learning library for the Lua
(programming language)|Lua
programming language
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Torch (machine learning)
1
and a scientific computing framework with
wide support for machine learning
algorithms. It uses a fast scripting
language LuaJIT, and an underlying C
(programming language)|C
implementation.
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Torch (machine learning) - torch
1
The core package of Torch is
[https://github.com/torch/torch7
torch]
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Torch (machine learning) - torch
1
The following exemplifies using
torch via its REPL interpreter:
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Torch (machine learning) - torch
1
It also has StochasticGradient class
for training a neural network using
Stochastic gradient descent, although
the Optim package provides much
more options in this respect, like
momentum and weight decay
Regularization
(mathematics)|regularization.
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Torch (machine learning) - Other packages
Many packages other than the above
official packages are used with Torch.
These are listed in the
[https://github.com/torch/torch7/wiki/Ch
eatsheet torch cheatsheet]. These extra
packages provide a wide range of
utilities such as parallelism,
asynchronous input/output, image
processing, and so on.
1
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Torch (machine learning) - Applications
Torch is used by DeepMind
Technologies|Google
DeepMind,[http://blog.mikiobraun.de/2014/
01/what-deepmind-google.html What is
going on with DeepMind and Google?]
1
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Torch (machine learning) - Applications
1
the Facebook AI Research Group,[http://www.kdnuggets.com/2014/02/exclusiveyann-lecun-deep-learning-facebook-ai-lab.html KDnuggets Interview with Yann
LeCun, Deep Learning Expert, Director of Facebook AI Lab] the Computational
Intelligence, Learning, Vision, and Robotics Lab at
NYU,[http://cilvr.nyu.edu/doku.php?id=code:start CILVR Lab Software]
MADBITS,[http://code.madbits.com/wiki/doku.php Machine Learning with
Torch7] IBM,[https://news.ycombinator.com/item?id=7928738 Hacker News]
Yandex[https://www.facebook.com/yann.lecun/posts/10152077631217143?comm
ent_id=10152089275552143offset=0total_comments=6 Yann Lecun's FaceBook
Page] and the Idiap Research Institute.[https://www.idiap.ch/scientificresearch/resources/torch IDIAP Research Institute : Torch] It is used and cited in
240 research
papers.[http://scholar.google.ca/scholar?cites=9993075313749753697as_sdt=2005
sciodt=0,5hl=en Google Scholar results for Torch: a modular machine learning
software library citations] For comparison, Theano (software)|Theano, a similar
library written in Python (programming language), C and CUDA, has 138
citations.[http://scholar.google.ca/scholar?cites=8194189194999260817as_sdt=20
05sciodt=0,5hl=en Theano: a CPU and GPU math expression compiler] Torch has
been extended for use on Android (operating
system)|Android[https://github.com/soumith/torch-android Torch-android
GitHub repository] and iOS.[https://github.com/clementfarabet/torch-ios Torchios GitHub repository] It has been used to build hardware implementations for
data flows like those found in neural
networks.[http://pub.clement.farabet.net/ecvw11.pdf NeuFlow: A Runtime
Reconfigurable Dataflow Processor for Vision]
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Overfitting - Machine learning
The concept of overfitting is
important in machine learning
1
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Overfitting - Machine learning
1
As a simple example, consider a database
of retail purchases that includes the item
bought, the purchaser, and the date and
time of purchase. It's easy to construct a
model that will fit the training set perfectly
by using the date and time of purchase to
predict the other attributes; but this model
will not generalize at all to new data,
because those past times will never occur
again.
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Overfitting - Machine learning
Generally, a learning algorithm is
said to overfit relative to a simpler one
if it is more accurate in fitting known
data (hindsight) but less accurate in
predicting new data (foresight)
1
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Training set - Use in artificial intelligence, machine learning, and statistics
In artificial intelligence or machine
learning, a training set consists of an input
Array data structure|vector and an answer
vector, and is used together with a
supervised learning method to train a
knowledge database (e.g. a neural net or
a naive bayes classifier) used by an AI
machine.
1
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Training set - Use in artificial intelligence, machine learning, and statistics
In statistics|statistical modeling, a
training set is used to fit a model that
can be used to predict a response
value from one or more predictors. The
fitting can include both feature
selection|variable selection and
parameter estimation
theory|estimation. Statistical models
used for prediction are often called
regression analysis|regression models,
1
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Training set - Use in artificial intelligence, machine learning, and statistics
1
In these fields, a major emphasis is placed
on avoiding overfitting, so as to achieve
the best possible performance on an
independent test set that follows the same
probability distribution as the training set.
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Tanagra (machine learning)
1
'Tanagra' is a free suite of machine learning
software for research and academic purposes
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Tanagra (machine learning)
developed by Ricco
Rakotomalala at the
Lumière University Lyon
2, France.
1
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Tanagra (machine learning)
1
Tanagra supports several standard data
mining tasks such as: Visualization,
Descriptive statistics, Instance
selection, feature selection, feature
construction, regression
analysis|regression, factor analysis,
data clustering|clustering, statistical
classification|classification and
association rule learning.
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Tanagra (machine learning)
Tanagra is an
academic project
1
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Tanagra (machine learning) - History
The development of Tanagra was
started in June 2003. The first version
is distributed in December 2003.
Tanagra is the successor of Sipina,
another free data mining tool which is
intended only for the supervised
learning tasks (classification),
especially an interactive and visual
construction of decision trees. Sipina is
still available online and is maintained.
1
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Tanagra (machine learning) - History
1
Tanagra is an open source project as
every researcher can access to the
source code, and add his own
algorithms, as far as he agrees and
conforms to the software distribution
license.
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Tanagra (machine learning) - History
1
The main purpose of Tanagra project is to
give researchers and students a userfriendly data mining software, conforming
to the present norms of the software
development in this domain (especially in
the design of its GUI and the way to use
it), and allowing to analyze either real or
synthetic data.
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Tanagra (machine learning) - History
From 2006, Ricco Rakotomalala made an
important documentation effort. A large
number of tutorials are published on a
dedicated website. They describe the
statistical and machine learning methods and
their implementation with Tanagra on real
case studies. The use of the other free data
mining tools on the same problems is also
widely described. The comparison of the
tools enables to the readers to understand
the possible differences in the presenting of
results.
1
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Tanagra (machine learning) - Description
1
Each node is a statistical or machine
learning technique, the connection
between two nodes represents the data
transfer
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Tanagra (machine learning) - Description
Tanagra makes a good compromise
between the statistical approaches (e.g.
parametric and nonparametric statistical
tests), the multivariate analysis methods
(e.g. factor analysis, correspondence
analysis, cluster analysis, regression) and
the machine learning techniques (e.g.
neural network, support vector machine,
decision trees, random forest).
1
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Music Information Retrieval - Statistics and Machine Learning
1
*Computational methods for classification,
clustering, and modelling — musical
feature extraction for mono- and
polyphonic music, similarity and pattern
matching, retrieval
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Music Information Retrieval - Statistics and Machine Learning
* Formal methods and databases —
applications of automated music
identification and recognition, such as
score following, automatic
accompaniment, routing and filtering
for music and music queries, query
languages, standards and other
metadata or protocols for music
information handling and information
retrieval|retrieval, multi-agent
1
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Music Information Retrieval - Statistics and Machine Learning
1
*Software for music information retrieval
— Semantic Web and musical digital
objects, intelligent agents, collaborative
software, web-based search and semantic
retrieval, query by humming, acoustic
fingerprinting
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Music Information Retrieval - Statistics and Machine Learning
* Music analysis and knowledge
representation — automatic
summarization, citing, excerpting,
downgrading, transformation, formal
models of music, digital scores and
representations, music indexing and
metadata.
1
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European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases
1
'ECML PKDD', the 'European Conference
on Machine Learning and Principles and
Practice of Knowledge Discovery in
Databases', is one of the leading ECML is
number 4 on the list. Both ECML and
PKDD are ranked on “tier A”. academic
conferences on machine learning and
knowledge discovery, held in Europe every
year.
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European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases - History
1
ECML PKDD is a merger of two European
conferences, 'European Conference on
Machine Learning' ('ECML') and 'European
Conference on Principles and Practice of
Knowledge Discovery in Databases'
('PKDD'). ECML and PKDD have been colocated since 2001; however, both ECML and
PKDD retained their own identity until 2007.
For example, the 2007 conference was
known as “the 18th European Conference on
Machine Learning (ECML) and the 11th
European Conference
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European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases - History
The history of ECML dates back to
1986, when the European Working
Session on Learning was first held. In
1993 the name of the conference was
changed to European Conference on
Machine Learning.
1
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European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases - History
PKDD was first organised in 1997.
Originally PKDD stood for the
European Symposium on Principles of
Data Mining and Knowledge Discovery
from Databases.. The name European
Conference on Principles and Practice
of Knowledge Discovery in Databases
was used since 1999..
1
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Feature (machine learning)
In machine learning and pattern
recognition, a 'feature' is an individual
measurable heuristic property of a
phenomenon being observed. Choosing
discriminating and independent features is
key to any pattern recognition algorithm
being successful in classification (machine
learning)|classification. Features are
usually numeric, but structural features
such as string (computer science)|strings
1
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Feature (machine learning)
The set of features of a given data
instance is often grouped into a feature
vector. The reason for doing this is
that the vector can be treated
mathematically. For example, many
algorithms compute a score for
classifying an instance into a particular
category by linearly combining a
feature vector with a vector of weights,
using a linear predictor function.
1
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Feature (machine learning)
1
The concept of feature is essentially the
same as the concept of explanatory
variable used in statistics|statistical
techniques such as linear regression.
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Feature (machine learning) - Classification
1
While different areas of pattern recognition
obviously have different features, once the
features are decided, they are classified
by a much smaller set of algorithms.
These include k-nearest neighbor
algorithm|nearest neighbor classification in
multiple dimensions, neural networks or
statistical classification|statistical
techniques such as Bayesian
inference|Bayesian approaches.
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Feature (machine learning) - Examples
In character recognition, features may
include horizontal and vertical profiles,
number of internal holes, stroke detection
and many others.
1
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Feature (machine learning) - Examples
In speech recognition, features for
recognizing phonemes can include noise
ratios, length of sounds, relative power,
filter matches and many others.
1
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Feature (machine learning) - Examples
1
In spam (electronic)|spam detection
algorithms, features may include
whether certain email headers are
present or absent, whether they are
well formed, what language the email
appears to be, the grammatical
correctness of the text, Markovian
frequency analysis and many others.
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Feature (machine learning) - Examples
In all these cases, and many others,
feature extraction|extracting features that
are measurable by a computer is an art,
and with the exception of some neural
networking and genetic techniques that
automatically intuit features, hand
selection of good features forms the basis
of almost all classification algorithms.
1
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Regularization (mathematics) - Regularization in statistics and machine learning
The most common variants in
machine learning are and
regularization, which can be added to
learning algorithms that minimize a
loss function by instead minimizing ,
where is the model's weight vector, ‖·‖
is either the norm or the squared
norm, and α is a free parameter that
needs to be tuned empirically
(typically by Cross-validation
1
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Regularization (mathematics) - Regularization in statistics and machine learning
regularization is often preferred
because it produces sparse models
and thus performs feature selection
within the learning algorithm, but
since the norm is not differentiable, it
may require changes to learning
algorithms, in particular gradientbased learners.
1
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Regularization (mathematics) - Regularization in statistics and machine learning
Bayesian model comparison|Bayesian
learning methods make use of a prior
probability that (usually) gives lower
probability to more complex models. Wellknown model selection techniques include
the Akaike information criterion (AIC),
minimum description length (MDL), and the
Bayesian information criterion (BIC).
Alternative methods of controlling overfitting
not involving regularization include crossvalidation (statistics)|cross-validation.
1
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Regularization (mathematics) - Regularization in statistics and machine learning
1
Regularization can be used to fine tune
model complexity using an augmented
error function with cross-validation
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Regularization (mathematics) - Regularization in statistics and machine learning
1
Examples of applications of different methods of
regularization to the linear model are:
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Regularization (mathematics) - Regularization in statistics and machine learning
1
A linear combination of the LASSO and ridge
regression methods is elastic net regularization.
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Classification in machine learning
1
In machine learning and statistics,
'classification' is the problem of
identifying to which of a set of
categorical data|categories (subpopulations) a new observation
belongs, on the basis of a training set
of data containing observations (or
instances) whose category
membership is known
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Classification in machine learning
In the terminology of machine
learning, classification is considered
an instance of supervised learning,
i.e. learning where a training set of
correctly identified observations is
available. The corresponding
unsupervised learning|unsupervised
procedure is known as cluster
analysis|clustering, and involves
grouping data into categories based
on some measure of inherent
similarity or distance.
1
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Classification in machine learning
1
Often, the individual observations are
analyzed into a set of quantifiable
properties, known variously
explanatory variables, features, etc
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Classification in machine learning
An algorithm that implements
classification, especially in a concrete
implementation, is known as a
'Pattern recognition|classifier'. The
term classifier sometimes also refers
to the mathematical function
(mathematics)|function,
implemented by a classification
algorithm, that maps input data to a
category.
1
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Classification in machine learning
1
In machine learning, the observations are
often known as instances, the explanatory
variables are termed features (grouped
into a feature vector), and the possible
categories to be predicted are classes
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Classification in machine learning - Relation to other problems
1
Classification and clustering are examples
of the more general problem of pattern
recognition, which is the assignment of
some sort of output value to a given input
value
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Classification in machine learning - Relation to other problems
A common subclass of
classification is probabilistic
classification
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Classification in machine learning - Relation to other problems
*It can output a confidence value
associated with its choice (in general,
a classifier that can do this is known
as a confidence-weighted classifier).
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Classification in machine learning - Relation to other problems
*Correspondingly, it can abstain when its
confidence of choosing any particular output is
too low.
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Classification in machine learning - Relation to other problems
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*Because of the probabilities which are
generated, probabilistic classifiers can
be more effectively incorporated into
larger machine-learning tasks, in a way
that partially or completely avoids the
problem of error propagation.
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Classification in machine learning - Frequentist procedures
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Early work on statistical
classification was
undertaken by Fisher,R
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Classification in machine learning - Bayesian procedures
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Unlike frequentist procedures, Bayesian
classification procedures provide a natural
way of taking into account any available
information about the relative sizes of the
sub-populations associated with the
different groups within the overall
population.Binder, D.A
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Classification in machine learning - Bayesian procedures
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Some Bayesian procedures involve the
calculation of class membership
probabilities|group membership
probabilities: these can be viewed as
providing a more informative outcome of a
data analysis than a simple attribution of a
single group-label to each new
observation.
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Classification in machine learning - Binary and multiclass classification
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Classification can be thought of as two
separate problems – binary classification
and multiclass classification
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Classification in machine learning - Linear classifiers
A large number of algorithms for
classification can be phrased in terms of a
linear function that assigns a score to each
possible category k by linear
combination|combining the feature vector
of an instance with a vector of weights,
using a dot product. The predicted
category is the one with the highest score.
This type of score function is known as a
linear predictor function and has the
following general form:
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Classification in machine learning - Linear classifiers
where 'X'i is the feature vector for
instance i, 'beta;'k is the vector of
weights corresponding to category k,
and score('X'i, k) is the score
associated with assigning instance i to
category k. In discrete choice theory,
where instances represent people and
categories represent choices, the
score is considered the utility
associated with person i choosing
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Classification in machine learning - Linear classifiers
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Algorithms with this basic setup are known
as linear classifiers. What distinguishes
them is the procedure for determining
(training) the optimal weights/coefficients
and the way that the score is interpreted.
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Classification in machine learning - Linear classifiers
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Examples of such
algorithms are
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Classification in machine learning - Linear classifiers
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*Logistic regression and
multinomial logit
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Classification in machine learning - Algorithms
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Examples of classification
algorithms include:
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Classification in machine learning - Algorithms
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**Least squares support
vector machines
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Classification in machine learning - Algorithms
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* Variable kernel density estimation#Use for
statistical classification|Kernel estimation
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Classification in machine learning - Evaluation
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Classifier performance depends greatly on the
characteristics of the data to be classified
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Classification in machine learning - Evaluation
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The measures precision and recall are
popular metrics used to evaluate the
quality of a classification system. More
recently, receiver operating
characteristic (ROC) curves have been
used to evaluate the tradeoff between
true- and false-positive rates of
classification algorithms.
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Classification in machine learning - Evaluation
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As a performance metric, the uncertainty
coefficient has the advantage over simple
accuracy in that it is not affected by the
relative sizes of the different classes.
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Classification in machine learning - Evaluation
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Further, it will not penalize an algorithm for simply
rearranging the classes.
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Classification in machine learning - Application domains
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Classification has many applications. In
some of these it is employed as a data
mining procedure, while in others more
detailed statistical modeling is undertaken.
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Classification in machine learning - Application domains
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* Drug discovery and Drug
development|development
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Classification in machine learning - Application domains
** Quantitative
structure-activity
relationship
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Classification in machine learning - Application domains
* Statistical natural
language processing
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Classification in machine learning - Application domains
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* Document classification
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Cognitive bias mitigation - Machine learning
Machine learning, a branch of
artificial intelligence, has been used
to investigate human learning and
decision making.Sutton, R. S., Barto,
A. G. (1998). MIT CogNet Ebook
Collection; MITCogNet 1998, Adaptive
Computation and Machine Learning,
ISBN 978-0-262-19398-6.
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Cognitive bias mitigation - Machine learning
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One technique particularly applicable to
Cognitive Bias Mitigation is neural
network|neural network learning and
choice selection, an approach inspired by
the imagined structure and function of
actual neural networks in the human brain
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Cognitive bias mitigation - Machine learning
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In principle, such models are capable
of modeling decision making that
takes account of human needs and
motivations within social contexts,
and suggest their consideration in a
theory and practice of Cognitive Bias
Mitigation
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ConceptNet - Machine learning tools
The information in ConceptNet can be
used as a basis for machine learning
algorithms. One representation, called
AnalogySpace, uses singular value
decomposition to generalize and represent
patterns in the knowledge in
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ConceptNet - Machine learning tools
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ConceptNet, in a way that can be used in
AI applications. Its creators distribute a
Python machine learning toolkit called
Divisi for performing machine learning
based on text corpora, structured
knowledge bases such as ConceptNet,
and combinations of the two.
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Learning algorithms - Machine learning and data mining
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* Machine learning focuses on prediction, based on
known properties learned from the training data.
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Learning algorithms - Machine learning and data mining
* Data mining focuses on the discovery
(observation)|discovery of (previously)
unknown properties in the data. This is the
analysis step of Knowledge
discovery|Knowledge Discovery in
Databases.
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Learning algorithms - Machine learning and data mining
Much of the confusion between these two
research communities (which do often have
separate conferences and separate journals,
ECML PKDD being a major exception)
comes from the basic assumptions they work
with: in machine learning, performance is
usually evaluated with respect to the ability to
reproduce known knowledge, while in
Knowledge Discovery and Data Mining (KDD)
the key task is the discovery of previously
unknown knowledge
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Classification (machine learning) - Feature vectors
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Most algorithms describe an individual
instance whose category is to be
predicted using a feature vector of
individual, measurable properties of the
instance
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Classification (machine learning) - Feature vectors
The vector space associated with
these vectors is often called the
feature space. In order to reduce the
dimensionality of the feature space, a
number of dimensionality reduction
techniques can be employed.
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Ground truth - Statistics and Machine Learning
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In machine learning, the term ground
truth refers to the accuracy of the
training set's classification for
supervised learning techniques. This
is used in statistical models to prove or
disprove research
hypothesis|hypotheses. The term
ground truthing refers to the process of
gathering the proper objective data for
this test. Compare with gold standard
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Ground truth - Statistics and Machine Learning
Bayesian spam filtering is a common
example of supervised learning. In this
system, the algorithm is manually taught
the differences between spam and nonspam. This depends on the ground truth
of the messages used to train the
algorithm; inaccuracies in that ground truth
will correlate to inaccuracies in the
resulting spam/non-spam verdicts.
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