An internationally recognized node of machine learning researchers at the University of Alberta

Promotes curiosity-driven Machine Learning research, and leading edge scientific and commercial applications in the bioinformatics and interactive entertainment industries.

Resarch related to machine learning includes neural networks, automata induction, computational learning theory, data mining, knowledge discovery, bioinformatics.

Research projects on collective intelligence, surface modeling, autoclass, Bayesian search.

Analysis of functional genomics data, Construction of data-dependent metrics for focusing data analysis on relevant or important aspects of the data.

Large group with projects in robot learning, data mining for manufacturing and in multimedia databases, causal inference, and disclosure limitation.

Research on theory of learning, neuroscience, bioinformatics & functional genomics, information extraction in text & multimedia, object detection/recognition, man-machines interfaces, virtual financial markets.

Developing theories and systems pertaining to intelligent behavior using a unified methodology. At the heart of the approach is the idea that learning has a central role in intelligence.

The group develops theories and systems pertaining to intelligent behavior using a unified methodology. At the heart of the approach is the idea that learning has a central role in intelligence.

CCLS investigates machine learning and data mining and their application to natural language understanding, the World Wide Web, bioinformatics, systems security and other emerging areas.

Techniques include inductive logic programming, model based reasoning, evolutionary computing, neural networks, multivariate statistics. Applications to drig design, protein secondary structure prediction, functional genomics, etc.

Research on kernel methods, support vector machines, neural networks, machine vision, bioinformatics, computational learning theory.

Pursues research on algorithms and software tools for gleaning knowledge from data and their applications in Bioinformatics, Security Informatics, Medical Informatics, Geoinformatics, Chemical Informatics, Semantic Web, e-Government, e-Enterprises, e-Commerce, and e-Science.

Research on machine learning theory, kernel methods for text analysis, support vector machines, kernel theory.

An on-line handwriting recognition engine based upon statistical dynamic time warping (SDTW) and support vector machines with a Gaussian DTW kernel (SVM-GDTW).

Research on neural computational theories of perception and action, with an emphasis on learning.

Tutorials, software, online books and articles on forecasting and systems modeling, optimization in expert systems, pattern recognition, data mining and knowledge discovery, from a research group at the Glushkov Institute of Cybernetics.

a group of researchers interested in artificial intelligence, computer supported collaborative learning and grid computing

Research on Support Vector Machines, Hidden Markov Models, fusion of generative and discriminative approaches, logical data analysis, large scale data analysis.

The IDA group is concerned with learning systems for intelligent data analysis. In particular, we are developing tools for high-dimensional multivariate statistics based on methods originally developed in the field of statistics and, more recently, in the neural networks and machine learning community.

Focuses on theory of logic and learning, and applied intelligent systems. Methodolgies range from traditional knowledge-based systems and neural networks to machine learning, agents, and evolutionary computation.

Research on symbolic and numerical approaches to machine learning, first order logic, intelligent document processing, spatial data mining, human-computer interaction.

Research projects mainly focused on text: Intelligent Information Access, Text Summarization, Text Analysis for Knowledge Acquisition.

Software systems that learn user preferences, Robot learning, text learning, generic learning methods.

Research on modeling high-dimensional data, learning hyper-parameters, boosting of neural networks, Markovian models, data mining, and other areas related to neural networks.

Information on their members, research areas, publications, teaching, and resources. Focus is on: data mining and knowledge discovery in databases, inductive logic programming, knowledge intensive learning, concept drift and context-sensitive learning, minimum description length principle, machine learning and music.

Research on learning first-order classification rules, first-order concept descriptions, genetic algorithms, neural networks, computational learning theory.

Research on Theories of Learning, Inference, and Discovery Data Mining and Knowledge Discovery, User Modeling and Intrusion Detection, Non-Darwinian Evolutionary Computation, Machine Vision through Learning, Education.

Research on Data Mining, Machine Learning,Inductive Logic Programming, Relational Learning, Machine Learning for Bioinformatics.

Research on adaptive processing of data structures, document analysis and technologies, natural language, machine learning for the web, visual databases, biochemistry and bioinformatics.

Research in Data mining, Inductive Logic Programming, Learning In Agents.

Research on decision theory, neural networks, computational biology, computational geometry, theoretical computer science, on-line learning algorithms, computational learning theory, reinforcement learning.

Research on inductive logic programming for natural language processing and for knowledge discovery in databases.

Research at UCI spans the spectrum of models for learning, including those based on statistics, logic, mathematics, neural structures, information theory, and heuristic search algorithms.

Research on higher-order concept learning, inductive logic programming, multi-agent learning systems, integration of prior knowledge, induction and deduction, incremental learning, hybrid symbolic/connectionist approaches, evolutionary strategies.

Offers WEKA, a comprehensive, open-source (GPL) machine learning and data mining toolkit in Java with classification, regression, clustering, and association rules. Command-line and GUI interfaces.

Research projects on learning in human-machine interaction, natural language interface to the WWW, statistical analysis of neurophysiological data, self-organization of proteins, nonlinear acoustic signal processing.

Research on Neural Networks and Decision Trees.

Research on General Inductive Learning, Inductive Logic Programming, Natural Language Learning, Qualitative Modeling & Diagnosis, Learning for Planning and Problem Solving. Recommender Systems and Text Categorization Student Modeling for Intelligent Tutoring Systems Text Data Mining Theory and Knowledge Refinement.

Research on information retrieval and extraction, bioinformatics, connectionist models, hybrid systems.

Uses partially observable Markov decision processes (POMDPs) as a basic framework for multi-agent planning

Archive of software, white papers, and research surveys maintained by a research lab at the National Center for Supercomputing Applications (NCSA)

Introduction by Hajime Fujita to POMDPs as a representation for multi-agent learning

Develops algorithms and representations for efficient pattern matching. Applications include face recognition, fingerprint identification, image analysis, 3-D model construction and visualization, and robot navigation.

Research on computational machine learning tools and theoretical frameworks with applications in computational molecular biology, computer vision, sensory processing, and iterative decoding.

The group focuses on probabilistic and information theoretic approaches to machine learning problems.

Research on Localization and Mapping, Partially Observable Markov Decision Processes, Computer Vision and Image Processing, Robot Architectures and Programming Languages, Learning Algorithms.

Applications of soft computing (fuzzy systems, neural networks, and genetic algorithms) in machine learning. Manuscripts and MATLAB codes related to fuzzy clustering and classification, and visualization and analysis of high-dimensional data.

Knowledge-based concepts, tools, and methods, and their applications, including: fuzzy systems, neural networks, genetic algorithms, machine learning, and natural language processing.

Research on Data Mining, Active Learning & Exploration, Reinforcement Learning for Decision and Control.

Applied research in data mining, knowledge discovery, pattern recognition, and automated classification and clustering.

ESPRIT working group on Neural and Computational Learning Theory. Partners, projects, publications archive.