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Teuvo Kohonen
Dr. Eng., Emeritus Professor of the Academy
of Finland; Academician
Since the 1960s, Professor Kohonen has introduced several new concepts to neural computing: fundamental theories
of distributed associative memory and optimal associative mappings, the learning subspace method, the selforganizing feature maps (SOMs), the learning vector quantization (LVQ), novel algorithms for symbol processing like
the redundant hash addressing, dynamically expanding context and a special SOM for symbolic data, and a SOM called
the Adaptive-Subspace SOM (ASSOM) in which invariant-feature filters emergence. A new SOM architecture WEBSOM
has been developed in his laboratory for exploratory textual data mining. In the largest WEBSOM implemented so far,
about seven million documents have been organized in a one-million neuron network: for smaller WEBSOMs, see the
demo at http://websom.hut.fi/websom/ .
Gender detection
The classification or description scheme is usually based on the
availability of a set of patterns that have already been classified or
described. This set of patterns is termed the training set, and the
resulting learning strategy is characterized as supervised learning.
Learning can also be unsupervised, in the sense that the system is
not given an a priori labeling of patterns, instead it itself establishes
the classes based on the statistical regularities of the patterns.
The classification or description scheme usually uses one of the following
approaches:
statistical (or decision theoretic) or
syntactic (or structural).
Statistical pattern recognition is based on statistical characterizations of
patterns, assuming that the patterns are generated by a probabilistic
system.
Syntactical (or structural) pattern recognition is based on the structural
interrelationships of features. A wide range of algorithms can be applied
for pattern recognition, from simple naive Bayes classifiers and neural
networks to the powerful KNN decision rules.
Pattern recognition is more complex when templates are used to
generate variants. For example, in English, sentences often follow the
"N-VP" (noun - verb phrase) pattern, but some knowledge of the
English language is required to detect the pattern.
Pattern recognition is studied in many fields, including psychology,
ethology, cognitive science and computer science.
Holographic associative memory is another type of pattern matching
where a large set of learned patterns based on cognitive meta-weight
is searched for a small set of target patterns.
What is a Pattern?
“A pattern is the opposite of a chaos; it is an
entity vaguely defined, that could be given a
name.” (Watanabe)
Recognition
Identification of a pattern as a member of a
category we already know, or we are familiar with
– Classification (known categories)
– Clustering (learning categories)
Category “A”
Category “B”
Clustering
Classification
Handwritten Digit Recognition
Cat vs. Dog
Supervised Classification
Training samples are labeled
Unsupervised Classification
Training samples are unlabeled
Segmentation
Pattern Recognition
• Given an input pattern, make a decision
about the “category” or “class” of the pattern
• Pattern recognition is needed in designing
almost all automated systems
• Other related disciplines: data mining,
machine learning, computer vision, neural
networks, statistical decision theory
• This course will present various techniques
to solve P.R. problems and discuss their
relative strengths and weaknesses
How do we design similarity?
Intra-class Variability
The letter “T” in different typefaces
Same face under different expression, pose, illumination
Inter-class Similarity
Characters that look similar
Identical twins
Difficulties of Representation
• “How
do you instruct someone (or some computer)
to recognize caricatures in a magazine, let alone
find a human figure in a misshapen piece of work?”
• “A program that could distinguish between male
and female faces in a random snapshot would
probably earn its author a Ph.D. in computer
science.” (Penzias 1989)
• A representation could consist of a vector of realvalued numbers, ordered list of attributes, parts
and their relations….
Difficulties of Representation
How should we
model a face to
account for the
large intra-class
variability?
John P. Frisby, Seeing. Illusion, Brian and Mind, Oxford University Press, 1980
Pattern Class Model
• A mathematical or statistical description for each pattern
class (population); it is this class description that is learned
from samples
• Given a pattern, choose the best-fitting model for it;
assign the pattern to the class associated with the bestfitting model
Pattern Recognition System
• Domain-specific knowledge
– Acquisition, representation
• Data acquisition
– camera, ultrasound, MRI,….
• Preprocessing
– Image enhancement, segmentation
• Representation
– Features: color, shape, texture,…
• Decision making
– Statistical (geometric) pattern recognition
– Syntactic (structural) pattern recognition
– Artificial neural networks
• Post-processing; use of context