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

Research on Machine Learning in Robotics, Factory Automation, and Assistance Systems.

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

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.

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

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 machine learning theory, kernel methods for text analysis, support vector machines, kernel 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 kernel methods, support vector machines, neural networks, machine vision, bioinformatics, computational learning theory.

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.

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.

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.

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

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

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

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