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This page was last updated on October 7th, 2008
This page was last updated on October 7th, 2008
Research Groups (from Machine Learning)
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Machine Learning and Natural Language Processing Lab - Freiburg (Germany)
Research on Data Mining, Machine Learning,Inductive Logic Programming, Relational Learning, Machine Learning for Bioinformatics.
Cognitive Computation, Harvard University
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.
Institute for Process Control and Robotics - University of Karlsruhe (Germany)
Research on Machine Learning in Robotics, Factory Automation, and Assistance Systems.
Automated Learning Group - NASA
Research projects on collective intelligence, surface modeling, autoclass, Bayesian search.
Machine Learning Laboratory - UMass
Research on Neural Networks and Decision Trees.
The NeuroCOLT Project
ESPRIT working group on Neural and Computational Learning Theory. Partners, projects, publications archive.
LISA - Adaptive Computer Systems Laboratory - Université de Montréal
Research on modeling high-dimensional data, learning hyper-parameters, boosting of neural networks, Markovian models, data mining, and other areas related to neural networks.
Computational Biology Group - University of Wales
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.
Computational Intelligence Group - University of Bristol
Research on kernel methods, support vector machines, neural networks, machine vision, bioinformatics, computational learning theory.
Machine Learning Group - UCI
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.
Gatsby Computational Neuroscience Unit - University College London
Research on neural computational theories of perception and action, with an emphasis on learning.
Machine Learning at UC Santa Cruz
Research on decision theory, neural networks, computational biology, computational geometry, theoretical computer science, on-line learning algorithms, computational learning theory, reinforcement learning.
Bioinformatics and Learning Metrics group - Helsinki University of Technology
Analysis of functional genomics data, Construction of data-dependent metrics for focusing data analysis on relevant or important aspects of the data.
Machine Learning Group - University of Waikato
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.
Alberta Ingenuity Centre for Machine Learning (AICML)
Promotes curiosity-driven Machine Learning research, and leading edge scientific and commercial applications in the bioinformatics and interactive entertainment industries.
Machine Learning Group - University of Bristol
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.
Learning Lab at CMU, School of Computer Science
Software systems that learn user preferences, Robot learning, text learning, generic learning methods.
Intelligent Systems, University College London
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.
Machine Learning Group - Stockholm University
Research on inductive logic programming for natural language processing and for knowledge discovery in databases.
Machine Learning Research Group - UTCS
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
Machine Learning at the Katholieke Universiteit Leuven
Research in Data mining, Inductive Logic Programming, Learning In Agents.
Computational Learning - Royal Holloway, University of London
Research on machine learning theory, kernel methods for text analysis, support vector machines, kernel theory.
Computational Intelligence, Learning, & Discovery
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-Comm
Machine learning and Neural Networks group - Universities of Florence, Pisa, and Siena
Research on adaptive processing of data structures, document analysis and technologies, natural language, machine learning for the web, visual databases, biochemistry and bioinformatics.
Freiburg Recognition of ON-line HANDwriting (Frog On Hand)
An on-line handwriting recognition engine based upon statistical dynamic time warping (SDTW) and support vector machines with a Gaussian DTW kernel (SVM-GDTW).
The Auton Project
Research on Data Mining, Active Learning & Exploration, Reinforcement Learning for Decision and Control.
Center for Biological and Computational Learning - MIT
Research on theory of learning, neuroscience, bioinformatics & functional genomics, information extraction in text & multimedia, object detection/recognition, man-machines interfaces, virtual financial markets.
Machine Learning and Genetic Algorithms group - University of Turin
Research on learning first-order classification rules, first-order concept descriptions, genetic algorithms, neural networks, computational learning theory.
Machine Learning - ÖFAI
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, mi
Group Method of Data Handling (GMDH)
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.
Machine Learning Research Group - UW-Madison
Research on information retrieval and extraction, bioinformatics, connectionist models, hybrid systems.
NCSA Automated Learning Group (ALG)
Archive of software, white papers, and research surveys maintained by a research lab at the National Center for Supercomputing Applications (NCSA)
Software Competence Center, Hagenberg (SCCH): Division of Knowledge Based Technologies
Knowledge-based concepts, tools, and methods, and their applications, including: fuzzy systems, neural networks, genetic algorithms, machine learning, and natural language processing.
Intelligent Data Analysis Group at GMD FIRST
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 th
Artificial Intelligence Research Laboratory - Iowa State University
Resarch related to machine learning includes neural networks, automata induction, computational learning theory, data mining, knowledge discovery, bioinformatics.
Knowledge Acquisition & Machine Learning Lab - University of Bari
Research on symbolic and numerical approaches to machine learning, first order logic, intelligent document processing, spatial data mining, human-computer interaction.
Probabilistic and Statistical Inference - University of Toronto
Research on computational machine learning tools and theoretical frameworks with applications in computational molecular biology, computer vision, sensory processing, and iterative decoding.
Soft Computing in Machine Learning
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.
IDIAP machine Learning Group - Martigny (Switzerland)
Research on Support Vector Machines, Hidden Markov Models, fusion of generative and discriminative approaches, logical data analysis, large scale data analysis.
The Machine Learning Systems Group at JPL
Applied research in data mining, knowledge discovery, pattern recognition, and automated classification and clustering.
Pattern Recognition and Image Processing (PRIP) Lab, Michigan State University
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.
Center for Automated Learning and Discovery - CMU
Large group with projects in robot learning, data mining for manufacturing and in multimedia databases, causal inference, and disclosure limitation.
Cognitive Computation Group at UIUC
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.
Machine Learning Lab - The Hebrew University
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.
Modeling network routing as Partially Observable Markov Decision Processes (POMDPs)
Uses partially observable Markov decision processes (POMDPs) as a basic framework for multi-agent planning
Machine Learning and Inference Laboratory - GMU
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.
Knowledge Acquisition and Machine Learning Group - University of Ottawa
Research projects mainly focused on text: Intelligent Information Access, Text Summarization, Text Analysis for Knowledge Acquisition.
Robot Learning Laboratory - CMU
Research on Localization and Mapping, Partially Observable Markov Decision Processes, Computer Vision and Image Processing, Robot Architectures and Programming Languages, Learning Algorithms.
Research on Data Mining, Machine Learning,Inductive Logic Programming, Relational Learning, Machine Learning for Bioinformatics.
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.
Research on Machine Learning in Robotics, Factory Automation, and Assistance Systems.
Research projects on collective intelligence, surface modeling, autoclass, Bayesian search.
Research on Neural Networks and Decision Trees.
ESPRIT working group on Neural and Computational Learning Theory. Partners, projects, publications archive.
Research on modeling high-dimensional data, learning hyper-parameters, boosting of neural networks, Markovian models, data mining, and other areas related to neural networks.
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.
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 neural computational theories of perception and action, with an emphasis on learning.
Research on decision theory, neural networks, computational biology, computational geometry, theoretical computer science, on-line learning algorithms, computational learning theory, reinforcement learning.
Analysis of functional genomics data, Construction of data-dependent metrics for focusing data analysis on relevant or important aspects of the data.
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.
Promotes curiosity-driven Machine Learning research, and leading edge scientific and commercial applications in the bioinformatics and interactive entertainment industries.
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.
Software systems that learn user preferences, Robot learning, text learning, generic learning methods.
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 inductive logic programming for natural language processing and for knowledge discovery in databases.
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
Research in Data mining, Inductive Logic Programming, Learning In Agents.
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-Comm
Research on adaptive processing of data structures, document analysis and technologies, natural language, machine learning for the web, visual databases, biochemistry and bioinformatics.
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 Data Mining, Active Learning & Exploration, Reinforcement Learning for Decision and Control.
Research on theory of learning, neuroscience, bioinformatics & functional genomics, information extraction in text & multimedia, object detection/recognition, man-machines interfaces, virtual financial markets.
Research on learning first-order classification rules, first-order concept descriptions, genetic algorithms, neural networks, computational learning theory.
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, mi
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 information retrieval and extraction, bioinformatics, connectionist models, hybrid systems.
Archive of software, white papers, and research surveys maintained by a research lab at the National Center for Supercomputing Applications (NCSA)
Knowledge-based concepts, tools, and methods, and their applications, including: fuzzy systems, neural networks, genetic algorithms, machine learning, and natural language processing.
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 th
Resarch related to machine learning includes neural networks, automata induction, computational learning theory, data mining, knowledge discovery, bioinformatics.
Research on symbolic and numerical approaches to machine learning, first order logic, intelligent document processing, spatial data mining, human-computer interaction.
Research on computational machine learning tools and theoretical frameworks with applications in computational molecular biology, computer vision, sensory processing, and iterative decoding.
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.
Research on Support Vector Machines, Hidden Markov Models, fusion of generative and discriminative approaches, logical data analysis, large scale data analysis.
Applied research in data mining, knowledge discovery, pattern recognition, and automated classification and clustering.
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.
Large group with projects in robot learning, data mining for manufacturing and in multimedia databases, causal inference, and disclosure limitation.
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.
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.
Uses partially observable Markov decision processes (POMDPs) as a basic framework for multi-agent planning
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 projects mainly focused on text: Intelligent Information Access, Text Summarization, Text Analysis for Knowledge Acquisition.
Research on Localization and Mapping, Partially Observable Markov Decision Processes, Computer Vision and Image Processing, Robot Architectures and Programming Languages, Learning Algorithms.
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