Research Groups Websites

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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.

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

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

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.

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.

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

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.

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

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.

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.

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

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

Research on General Inductive Learning, Inductive Logic Programming, Natural Language Learning, Qualitative Modeling and 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 Neural Networks and Decision Trees.

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.