Research Groups Machine Learning Artificial Intelligence

• - 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 Data Mining, Machine Learning,Inductive Logic Programming, Relational Learning, Machine Learning for Bioinformatics.
  
• - Research projects on collective intelligence, surface modeling, autoclass, Bayesian search.
  
• - 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.
  
• - Uses partially observable Markov decision processes (POMDPs) as a basic framework for multi-agent planning
  
• - Large group with projects in robot learning, data mining for manufacturing and in multimedia databases, causal inference, and disclosure limitation.
  
• - Promotes curiosity-driven Machine Learning research, and leading edge scientific and commercial applications in the bioinformatics and interactive entertainment industries.
  
• - 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
  
• - Applied research in data mining, knowledge discovery, pattern recognition, and automated classification and clustering.
  
• - Software systems that learn user preferences, Robot learning, text learning, generic learning methods.
  
• - Research on kernel methods, support vector machines, neural networks, machine vision, bioinformatics, computational learning theory.
  
• - 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 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 on computational machine learning tools and theoretical frameworks with applications in computational molecular biology, computer vision, sensory processing, and iterative decoding.
  
• - Research on symbolic and numerical approaches to machine learning, first order logic, intelligent document processing, spatial data mining, human-computer interaction.
  
• - ESPRIT working group on Neural and Computational Learning Theory. Partners, projects, publications archive.
  
• - Research on neural computational theories of perception and action, with an emphasis on learning.
  
• - Research on adaptive processing of data structures, document analysis and technologies, natural language, machine learning for the web, visual databases, biochemistry and bioinformatics.
  
• - 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
  
• - Resarch related to machine learning includes neural networks, automata induction, computational learning theory, data mining, knowledge discovery, bioinformatics.
  
• - Research on machine learning theory, kernel methods for text analysis, support vector machines, kernel theory.
  
• - 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.
  
• - 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
  
• - Research on information retrieval and extraction, bioinformatics, connectionist models, hybrid systems.
  
• - Research on Data Mining, Active Learning & Exploration, Reinforcement Learning for Decision and Control.
  
• - 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 learning first-order classification rules, first-order concept descriptions, genetic algorithms, neural networks, computational learning theory.
  
• - Research on theory of learning, neuroscience, bioinformatics & functional genomics, information extraction in text & multimedia, object detection/recognition, man-machines interfaces, virtual financial markets.
  
• - Research in Data mining, Inductive Logic Programming, Learning In Agents.
  
• - Research projects mainly focused on text: Intelligent Information Access, Text Summarization, Text Analysis for Knowledge Acquisition.
  
• - 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.
  
• - Analysis of functional genomics data, Construction of data-dependent metrics for focusing data analysis on relevant or important aspects of the data.
  
• - Research on Support Vector Machines, Hidden Markov Models, fusion of generative and discriminative approaches, logical data analysis, large scale data analysis.
  
• - 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.
  
• - The group focuses on probabilistic and information theoretic approaches to machine learning problems.
  
• - 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.
  
• - 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 inductive logic programming for natural language processing and for knowledge discovery in databases.
  
• - Research on modeling high-dimensional data, learning hyper-parameters, boosting of neural networks, Markovian models, data mining, and other areas related to neural networks.
  
• - Archive of software, white papers, and research surveys maintained by a research lab at the National Center for Supercomputing Applications (NCSA)
  
• - 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 Neural Networks and Decision Trees.
  
• - 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.
  
• - Knowledge-based concepts, tools, and methods, and their applications, including: fuzzy systems, neural networks, genetic algorithms, machine learning, and natural language processing.
  
• - An internationally recognized node of machine learning researchers at the University of Alberta
  
• - 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 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.
  
• - Introduction by Hajime Fujita to POMDPs as a representation for multi-agent learning
  
• - 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.
  
• - a group of researchers interested in artificial intelligence, computer supported collaborative learning and grid computing
  
• - Research on decision theory, neural networks, computational biology, computational geometry, theoretical computer science, on-line learning algorithms, computational learning theory, reinforcement learning.
  
• - 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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