Learning Distance Functions For Gene Expression Data
... • Reducing the big number of genes to the most discriminative genes
• Removing noisy, irrelevant and redundant information
• Learning from a small number of training instances
• Classification of binary and multiclass problems
• Learning with unequal distribution of classes in a dataset
• Comparison ...
... Chinese AI community into the world of international AI.
Reward-Related Responses in the Human Striatum
... A drawback of the early PET and fMRI studies was the limitations in the
experimental designs. For instance, although activation of the striatum was
observed in a video game where rewards were present,33 it is difficult to assess what the signal is due to (i.e., anticipation, delivery, or even magnit ...
. - Villanova Computer Science
... which don’t require training cases
– No prior definition of goal
– Typical aim is “put similar things together”
MSc_2011 - University of Alberta
... Developed components of the Microfossil Quest
prototype, a crowdsourcing approach evolved
from a computer-aided approach
Knowledge Acquisition and Learning by Experience
... However, there are also well-known problems related to the rule-based
approach. An example is the lack of robustness and flexibility in problem solving
due to the narrow and tailored scope of the knowledge. Another example is the
difficulties in maintaining and updating a system's knowledge over tim ...
... Closed book
Cover all quarter’s material
Emphasis on material not covered on midterm
• Even more emphasis on material not on any PS
AutoTutor - Google Sites
... developed by researchers affiliated with the Institute of Intelligent Systems at University of
Memphis, where AutoTutor was developed. Many of these projects were led by different
research collaborations, some of which spread across multiple institutions, and represent novel
contributions in their o ...
The Unconscious Mind as a Means for Authentication - E
... lead to identification of Imprints of Unconscious Processes – IUPs – and these could be
extracted to create an authentication.
The feasibility of IUPs as a new model for authentication led us to propose and begin
studying five main hypotheses15. We simplify here, but these hypotheses provided
a fram ...
MACHINE FASHION: AN ARTIFICIAL INTELLIGENCE BASED
... There might be a moral argument about whether people should be judged by their
apparel. In practice however, few people would consider a person in baggy jeans walking into
their first meetings seriously. Dressing properly brings a big ROI (Return of Investment).
According to a survey done by OfficeT ...
Dynamic Potential-Based Reward Shaping
... a taken in state s results in a transition to state s0 . The
problem of solving an MDP is to find a policy (i.e., mapping
from states to actions) which maximises the accumulated
reward. When the environment dynamics (transition probabilities and reward function) are available, this task can be
... j to neuron i. aj is the activation of a neuron j.
•The activation of each neuron is produced by using a
suitable threshold function and a threshold. For
example we can assume that the activations are binary
(i.e. either 0 or 1) and to achieve this we use the step
CS 476: Networks of Neural Computat ...
Neural constraints on learning
... existing network constrains the patterns that a subset of its neurons
is capable of exhibiting, and if so, what principles define this constraint. We employed a closed-loop intracortical brain–computer interface learning paradigm in which Rhesus macaques (Macaca mulatta)
controlled a computer cursor ...
Reports on the 2015 AAAI Workshop Series
... profoundly hearing impaired patients.
The workshop participants discussed how the
trend toward the development of new assistive technologies is growing to help people with disabilities
but still not ready to be adopted by them. Based on
this observation, the participants shared the goal of
Learning Abstract Planning Cases
... and ﬁne elements on the right side of the workpiece. However, the detailed shape
of those elements is completely diﬀerent. Since the abstract problem as stated in
the abstract cases C1a matches the abstraction of the new problem P2 completely,
the abstract solution from C1a can be used to solve the ...
The Importance of Cognitive Architectures
... centered on primitives of cognition as envisioned in the cognitive architecture, and therefore such
explanations are deeper explanations. Because of the nature of such deeper explanations, this style
of theorizing is also more likely to lead to unified explanations for a large variety of data and/or ...
Bayesian Network Classifiers
... variables in the data. The objective is to induce a network (or a set of networks) that “best
describes” the probability distribution over the training data. This optimization process is
implemented in practice by using heuristic search techniques to find the best candidate over
the space of possibl ...
Chapter 02 Strategic Decision Making
... A. By building models out of organizational information to lend insight into important
business issues and opportunities
B. By building models out of transactional information only to lend insight into important
business issues and opportunities
C. By building models out of analytical information on ...
Diagnosing Self-Efficacy in Intelligent Tutoring Systems: An
... as level of engagement [22, 25]. Thus, if an ITS could increase a student’s selfefficacy, then it could enable the student to be more actively involved in learning,
expend more effort, and be more persistent; it could also enable them to successfully
cope in situations where they experience learning ...
The Promise of Artificial Intelligence
... A vast and diverse array of applications use AI, with algorithms powering everything from smartphone apps that help
consumers with their holiday shopping, to accelerating the process of discovering new lifesaving drugs.23 Most uses of
AI have at least one of seven functions: monitoring; discovering; ...
Concept learning, also known as category learning, concept attainment, and concept formation, is largely based on the works of the cognitive psychologist Jerome Bruner. Bruner, Goodnow, & Austin (1967) defined concept attainment (or concept learning) as ""the search for and listing of attributes that can be used to distinguish exemplars from non exemplars of various categories."" More simply put, concepts are the mental categories that help us classify objects, events, or ideas, building on the understanding that each object, event, or idea has a set of common relevant features. Thus, concept learning is a strategy which requires a learner to compare and contrast groups or categories that contain concept-relevant features with groups or categories that do not contain concept-relevant features.Concept learning also refers to a learning task in which a human or machine learner is trained to classify objects by being shown a set of example objects along with their class labels. The learner simplifies what has been observed by condensing it in the form of an example. This simplified version of what has been learned is then applied to future examples. Concept learning may be simple or complex because learning takes place over many areas. When a concept is difficult, it is less likely that the learner will be able to simplify, and therefore will be less likely to learn. Colloquially, the task is known as learning from examples. Most theories of concept learning are based on the storage of exemplars and avoid summarization or overt abstraction of any kind.