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Machine Learning

Overview: Machine learning is the subfield of AI that studies the automated acquisition of domain-specific knowledge. The goal of these systems is to improve their performance as the result of experience. Studies in this field include problem classification and decomposition, principals of intelligence, reasoning, and natural language processing. Machine learning can be looked at as a framework for doing AI research and development. Five main areas of machine learning are: analytic learning methods; neural network (connectionist) learning methods; genetic algorithms and classifier systems; empirical methods for inducing rules and decision trees; and case-based approaches to learning.

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Machine Learning Information on the Internet
European Network of Excellence in Machine Learning (ML) (Mlnet) (17)  Coordinate machine learning research and development throughout Europe. This research focusing on scientific methods to enable machine learning to become an important technology for building future intelligent systems. Machine learning and knowledge acquisition can assist in the building of knowledge-bases. 
FOCL Machine Learning System University of California, Irvine Extends Quinlan's FOIL program by containing a compatible explanation-based learning component. Learning Horn Clause programs from examples and (optionally) background knowledge, FOCL is implemented in Common LISP and runs on a variety of machines. The Macintosh version of FOCL also contains a graphical interface that graphs the search space explored by FOCL, so it is a useful pedagogical tool for explaining inductive and explanation- based learning. In addition, there are facilities for creating and graphically editing knowledge-bases, tracing rules, and generating explanations, which allows the Mac version to be used as an expert system shell. 
LookSmart - Machine Learning Research Links to machine learning research groups.
Machine Learning Reference site to many other sources of information on machine learning including research groups, technologies, journals, applications, systems, and papers. 
Machine Learning at John Hopkins University Projects currently underway include OC1 decision tree system and PEBLS memory-based reasoning system. 
Machine Learning at the Austrian Research Institute for AI (OFAI) Vienna  Research being pursued include: inductive logic programming ML and qualitative modeling, multistrategy learning, concept drift constructive induction, and predicate invention knowledge acquisition, machine learning and music data mining, and knowledge discovery in databases (planned). 
Machine Learning at the University, Irvine Research includes forming concepts, learning search heuristics, learning how to decompose a problem into subproblems, improving motor skills, explaining new examples in terms of existing knowledge, revising and extending existing theories in light of new evidence, and discovering scientific laws and theories. The group is also looking towards producing integrated systems with learning components that interact in significant ways. Because the focus on learning incorporates issues of representation and performance, they are simultaneously exploring alternative schemes for representing knowledge -- rule- based systems, frame-like structures, probabilistic concepts, and neural networks -- along with processes that employ these different notations. 
eGroups: Machine-Learning An online mailing list for people interested in machine learning.
Machine Learning for Meteorology At the Naval Research Laboratory (NRL)  The Lab is working with Navy Center for Applied Research in AI (NCARAI) to design and build expert systems for meteorological analysis. 
Machine Learning Online Reference site to many other sources of information on machine learning made available by Kluwer Academic Publishers. Infromation includes information on the Internet for Logic Programming, Data Mining, Knowledge Discovery, and other technologies. 
Machine Learning Papers Two groups of papers -- those published (journals, conferences, books, etc.) and others (technical reports, submissions, etc.) -- are organized by year with the most recent year being first with a complete reference including an abstract. 
Machine Learning Research Group, University of Texas at Austin The focuses is on combining empirical and knowledge-based learning techniques, including applications such as natural language acquisition, problem solver speedup, diagnosis, qualitative modeling, and tutoring systems. 
Machine Learning Section of the Navy Center for Applied Research in AI This group performs basic research in several areas, including: case- based reasoning, concept learning from examples, inductive logic programming, genetic algorithms, and robotic learning and control. 
Occam Machine Learning System at the University of California, Irvine Occam is a learning system that includes similarity-based, theory- driven, and explanation-based learning programs; routines to store generalization and cases in a hierarchical memory; a pattern matcher and rule-based inference module; and a simple natural language parser and generator. It acquires causal and social knowledge by empirical techniques by exploiting inter-example and intra- example relationships. An explanation-based learning component of OCCAM takes advantage of prior knowledge to constrain the learning process. 
Online Machine Learning Resources Links to machine learning sites - categories include general ML information, machine learning and data mining software, benchmarks, papers, bibliographies, etc.
Will's Technical Page A collection of articles and other technical resources for artificial intelligence (especially data mining and machine learning).
Yahoo - Computer Science: Artificial Intelligence: Machine Learning Links to machine learning related topics such as agents, knowledge charing effort, archives, reinforcement learning and more. 

Sponsors of PC AI - Machine Learning Vendors
Provider of multi-strategy data mining suite PolyAnalyst provides the following machine learning algorithms: hybrid Neural Net-GMDH,
Symbolic Knowledge Acquisition Technology (SKAT), hybrid Genetic Algorithm-Memory Based Reasoning, new distribution-based Clustering, Classification, and Stepwise Linear Regression. A documented API library is available for developers.

The Haley Enterprise

Product information and literature. Specializing in automated reasoning and machine learning technologies. Emphasis is on products for programming as opposed to programming services or applications products. 

Other Machine Learning Vendors
AbTech Corporation AbTech Corporation may be reached by voice at (888)8ABTECH or (804)977-0686, by fax at (804)977-9615, or by email at sales@abtech.com 
Academic Distributing  Academic@aol.com 
Acquired Intelligence  sales@aiinc.bc.ca 
Attar Software Ltd. See the Attar Software website for more information about their machine learning package, XpertRule. 
IntelligenceWare  us000272@pop3.interramp.com 
The Schwartz Associates  TJS@cup.Portal.com 

Dynamic Search Engine References
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Article References
Machine Learning  Parsaye, K. (1989) PC AI, 3(4), 26. 
Collective Learning Systems I: Introduction  Bock, P. and Becker, G. (1992) PC AI, 6(4), 26-30. 
Collective Learning Systems II: The ALISA Image Analysis Engine  Bock, P. and Becker, G. (1993) PC AI, 7(1), 42-44. 
Collective Learning Systems III: Applications of ALISA  Becker, G. and Bock, P. (1994) PC AI, 8(1) , 34-36. 
Collective Learning Systems III  Bock, P. and Becker, G. (1994) PC AI, 8(1) , 34. 
What Does Your Company Really Do? Data Fusion in the Era of Knowledge Management Cox, E. (1999) PC AI, 13(4), 37
Square Pegs in Round Holes: Input Rotation for Enhancing Machine Learning Solutions Dwinnell, W. (1999) PC AI, 13(6), 24

Book References
Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems  Adeli, H. and Hung, S. (1995) New York, NY: John Wiley & Sons Inc, pps. 211. ISBN: 0-471-01633-0. 
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Algorithmic Learning  Hutchinson, A. (1994, 1995) Oxford University Press. ISBN: 0-19-853848-0. 
Neural Networks and Machine Learning (NATO Asi Series. Series F, Computer and Systems Sciences, Vol 168) Bishop, C. (ed.) (1998) Springer Verlag. ISBN 354064928X
Graphical Models for Machine Learning and Digital Communication (Adaptive Computation and Machine Learning) Frey, B. (1998) Bradford Books, pps. 200. ISBN 026206202X
Machine Learning Methods for Ecological Applications Fielding, A. (1999) Kluwer Academic Pub. ISBN 0412841908
Machine Learning : Ecml 2000 : 11th European Conference on Machine Learning, Barcelona, Catalonia, Spain, May 31-June 2, 2000 Proceedings De Mantaras, R. (ed.), et. al. (2000) Springer Verlag. ISBN 3540676023

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