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Sammut, Claude; Webb, Geoffrey I. (Eds.)

1st Edition., 2010, XXVI, 1032 p. 95 illus. in color., Hardcover

ISBN: 978-0-387-30768-8

  • Over 250 entries covering key concepts and terms in the broad field of machine learning. Entries include in-depth essays and definitions, historical background, key applications, and bibliographies
  • Extensive cross-references support efficient, user-friendly searchers for immediate access to useful information
  • Serves to open the field to those inquiring into this fast-growing area of research

This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the several hundred entries in this pre-eminent work include useful literature references, providing the reader with a portal to more detailed information on any given topic.

Topics for the "Encyclopedia of Machine Learning" were selected by a distinguished international advisory board. Each peer-reviewed, highly-structured entry includes a definition, key words, an illustration, applications, a bibliography, and links to related literature.

The style of the entries in the "Encyclopedia of Machine Learning" is expository and tutorial. This makes the book a practical resource for high-performance computing experts, as well as professionals in other fields, who need to access this vital information but may not have the time to work their way through an entire text on their topic of interest.

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TABLE OF CONTENTS

Clustering.- Statistical Machine Learning.- Statistical Language Learning.- Inductive Logic Programming.- Learning and Logic.- Meta-Learning.- ROC analysis.- Information Theory.- Instance-based Learning Time Series.- Policy Search and Active Selection.- Reinforcement Learning.- Artificial Neural Network.- Text Mining.- Machine Learning in Bioinformatics.- Rule Learning.- Evolutionary Computation.- Behavioral Cloning.- Search.- Computational Learning Theory.- Online Learning.- Learning Paradigms.- Model-based Reinforcement Learning.- Active Learning.- Explanation-based Learning.- Data Mining.- Graph Mining

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