Encyclopedia of Machine Learning

2010
ISBN: 978-0-387-34558-1
Verlag: Springer US

Medienkombination, Englisch, 1031 Seiten, Book + Online Access, Format (B × H): 193 mm x 260 mm

This comprehensive encyclopedia, with over 250 entries in an A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of machine learning. Most entries in this preeminent work include useful literature references.Topics for the Encyclopedia of Machine Learning were selected by a distinguished international advisory board. These peer-reviewed, highly-structured entries include definitions, illustrations, applications, bibliographies and links to related literature, providing the reader with a portal to more detailed information on any given topic.The style of the entries in the Encyclopedia of Machine Learning is expository and tutorial, making the book a practical resource for machine learning 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.The authoritative reference is published both in print and online. The print publication includes an index of subjects and authors. The online edition supplements this index with hyperlinks as well as internal hyperlinks to related entries in the text, CrossRef citations, and links to additional significant research.

Zielgruppe

Research

Autoren/Hrsg.

Sammut, Claude

Webb, Geoffrey I.

Inhaltsverzeichnis

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

Über Autor(innen)

Claude Sammut is a Professor of Computer Science and Engineering at the University of New South Wales, Australia, and Head of the Artificial Intelligence Research Group. He is the UNSW node Director of the ARC Centre of Excellence for Autonomous Systems and a member of the joint ARC/NH&MRC project on Thinking Systems. He is on the editorial boards of the Journal of Machine Learning Research, the Machine Learning Journal and New Generation Computing, and was the chairman of the 2007 International Conference on Machine Learning.Geoffrey I. Webb is research professor in the faculty of Information Technology at Monash University, Melbourne, Australia. He has published more than 150 scientific papers and is the author of the data mining software package Magnum Opus. His research areas include strategies for strengthening the Naïve Bayes machine learning technique, K-optimal pattern discovery, and work on Occam’s razor. He is editor-in-chief of Springer’s Data Mining and Knowledge Discovery journal, as well as being on the editorial board of Machine Learning.