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Learning the Structure of Factored Markov Decision Processes in
Learning the Structure of Factored Markov Decision Processes in

... To determine the best test to install at a decision node, decision tree induction algorithms are based on an information-theoric metric. This metric is computed for each attribute νi ∈ A. Different metrics have been proposed such as gain ratio, Kolmogorov-Smirnoff or χ2 . In spiti, we use χ2 because ...
- White Rose Research Online
- White Rose Research Online

... Most research on existing bagging-based sampling schemes for imbalanced data, e.g. (Li 2007; Hido, Kashima, and Takahashi 2009), focused on using sampling methods to provide a set of equally balanced or average-balanced training sub-sets for training classifiers to improve the performance of the pre ...
Artificial Intelligence (AI) Machine Learning and AI Pattern Recognition
Artificial Intelligence (AI) Machine Learning and AI Pattern Recognition

... Unsupervised learning: The goal of the machine is to build a model of x that can be used for reasoning, decision making, predicting things, communicating etc. Reinforcement learning: The machine can also produce actions a1, a2, . . . which affect the state of the world, and receives rewards (or puni ...
Full text
Full text

... Credit risk assessment, credit scoring and loan applications approvals are typical tasks that can be performed using machine learning or data mining techniques. Hence a lot of research has been carried out in this area. Chen et al. [7] use a two-stage approach composed of k-means clustering and supp ...
Object Focused Q-learning for Autonomous Agents
Object Focused Q-learning for Autonomous Agents

... Many other methods leverage different models of the world that are not based on objects. Some of these approaches rely on a hierarchical domain-specific structure that has to be specified by a system designer [2] or derived from heuristics that apply only to restricted classes of domains [8, 15, 17] ...
Short- and Long-Term Changes in Joint Co
Short- and Long-Term Changes in Joint Co

... synergy between the two hypothetical mechanisms. Previously observed decreases in electromyogram (EMG) might have other explanations, such as trajectory modifications that reduce joint torques. To circumvent such complications, we required strict trajectory control and examined only successful trial ...
Major Related Courses
Major Related Courses

... Major Related Courses (24 hours) Select 4 courses each from 2 of the following 3 Concentration Areas Core Courses for Psychology/HCI Concentration Area (select 12 hours from list of courses below) CGS 3325 Historical Perspectives: Mind and Machines Since 1600 CGS 4359 Cognitive-Neuroscience CGS 4362 ...
review - NYU Psychology
review - NYU Psychology

... understood without recognizing the role of other regions in the same fear learning circuit, this kind of learning cannot be completely understood without considering the intricacy of the natural environment in which it occurs. For example, fear conditioning procedures have traditionally examined lea ...
Tracking the Emergence of Conceptual Knowledge during Human
Tracking the Emergence of Conceptual Knowledge during Human

... conceptual knowledge and its effect on choice behavior. Critically, the hippocampus alone supported the efficient transfer of knowledge to a perceptually novel setting. These findings provide compelling evidence that the hippocampus supports conceptual learning through the networking of discrete mem ...
Selective Data Acquisition for Machine Learning.
Selective Data Acquisition for Machine Learning.

... training data, for instance, through cross validation. The available training examples themselves are often acquired through an active acquisition process. Here, due to the preferences of the active process, the distribution of data in the training pool may differ substantially from that of the nati ...
CV - Chris Gatti
CV - Chris Gatti

... ˆ Used simulated annealing to determine optimal musculoskeletal model parameters to best match experimental measurements and reduce model development time from days to minutes. ˆ Developed discrete event simulation of surgery scheduling and procedures to determine the effects of changes in surgery du ...
IOSR Journal of Computer Engineering (IOSR-JCE)
IOSR Journal of Computer Engineering (IOSR-JCE)

... communication costs. Because most techniques used in today's IDS are not able to deal with the dynamic and complex nature of cyber-attacks on computer networks. So the author reviewed several influential algorithms for intrusion detection based on various machine learning techniques, and divided the ...
LISTENING, IMAGINING, PERFORMING: MELODY AS A LIFE
LISTENING, IMAGINING, PERFORMING: MELODY AS A LIFE

... Copland in the low-frequency range without other voices or parts, conditions under which no interference arises. Perhaps the melody advantage is not specific to listening; to shed light on its origin, we consider when melody begins in the minds of its audience. Musical experience is often discussed ...
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Improving the scalability of rule

... Pittsburgh LCS. Several of BioHEL features have been inherited from GAssist. The system applies an almost standard generational GA, which evolves individuals that are classification rules. The learning process creates a rule set by iteratively learning one rule at a time using a GA. After each rule ...
The CHREST Architecture of Cognition The Role of
The CHREST Architecture of Cognition The Role of

... Thus, CHREST is highly selective, as presumably is the human cognitive system. This is consistent with research on perception and evolution, which has shown that animals and humans in particular have evolved powerful perceptual mechanisms for extracting key features from sensations in order to survi ...
PDF file
PDF file

... is called a diamond; and the number “6” rotated by 180◦ is called “9”. A type of complete representation scheme that is able to learn all open-ended tasks as well as their embedded invariances is a great challenge that this work aims at. Why invariance? For learning a single given task, invariant fe ...
Presentation
Presentation

... CEDEFOP • Affects national policies on validation • Prepared Common European principles for validation CEDEFOP • Prepared the European inventory on validation • European guidelines for validating CEDEFOP • Carries out research and comparative analysis ...
AI Magazine - Intelligent and Mobile Agents Research Group
AI Magazine - Intelligent and Mobile Agents Research Group

... the program was quite evenly split between papers from either a constraints or a machine-learning background. The mix of attendees from the key scientific topic areas of the workshop was a significant feature of the workshop and was commented on throughout the event. There were many shared interests ...
Artificial intelligence tools for software engineering
Artificial intelligence tools for software engineering

... Representing the meaning of a requirement or extracting the informational content conveyed by a sentence requires several kinds of knowledge and consequently several levels of analysis : - Morphological knowledge : first the words of a sentence, together with their canonical form, have to be identif ...
Tarek R. Besold, Kai
Tarek R. Besold, Kai

... We concede that this standard exceeds what our current engineering can reach. We have a more humble aim at present: engineer a computing machine able to convince non-experts, Turing-test style, that the output they observe is produced by what they would call a “creative” computing machine; this is c ...
Learning Efficient Markov Networks - Washington
Learning Efficient Markov Networks - Washington

... graphs [11]. We will use a more convenient form for our purposes, which we call feature graphs. Inference in feature graphs is linear in the size of the graph. For readers familiar with AND/OR graphs [11], a feature tree (or graph) is simply an AND/OR tree (or graph) with OR nodes corresponding to f ...
Advanced Applications of Neural Networks and Artificial Intelligence
Advanced Applications of Neural Networks and Artificial Intelligence

... long image sequences. Trajectory data is supplied by a tracker using Active Shape Models, from which a model of the distribution of typical trajectories is, learnt [23]. The techniques being developed will allow models of characteristic object behaviors to be learnt from the continuous observation o ...


... In case-based reasoning systems, adaptation of retrieved cases plays a crucial role in flexible reuse of stored experiences. However, despite the importance of case adaptation, the case adaptation process remains the least understood part of case-based reasoning. The difficulty of endowingcase-based ...
Connecting Conscious and Unconscious - Axel Cleeremans
Connecting Conscious and Unconscious - Axel Cleeremans

... latest work on “implicit learning,” a phenomenon first explored by Arthur Reber in 1967 whereby people are shown to be able to learn about novel information without intention to do so and without awareness of the underlying regularities. Broadbent’s ingenious experiments propelled the field in hithe ...
Intelligent Information Retrieval and Web Search
Intelligent Information Retrieval and Web Search

... Transfer Learning • Most machine learning methods learn each new task from scratch, failing to utilize previously learned knowledge. • Transfer learning concerns using knowledge acquired in a previous source task to facilitate learning in a related target task. • Usually assume significant training ...
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Concept learning

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.
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