Overview
- Presents 800 entries covering key concepts and terms in the broad field of machine learning
- Updates and informs through in-depth essays and definitions, historical background, key applications, and bibliographies
- Supports quick and efficient discovery of information through extensive cross-references
- Opens the field to those inquiring into this fast-growing area of research
- Includes supplementary material: sn.pub/extras
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About this book
This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining. A paramount work, its 800 entries - about 150 of them newly updated or added - are filled with valuable literature references, providing the reader with a portal to more detailed information on any given topic.
Topics for the Encyclopedia of Machine Learning and Data Mining include Learning and Logic, Data Mining, Applications, Text Mining, Statistical Learning, Reinforcement Learning, Pattern Mining, Graph Mining, Relational Mining, Evolutionary Computation, Information Theory, Behavior Cloning, and many others. Topics 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 entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. Machine learning and data mining techniques have countless applications, including data science applications, and this reference is essential for anyone seeking quick access to vital information on the topic.
Topics for the Encyclopedia of Machine Learning and Data Mining include Learning and Logic, Data Mining, Applications, Text Mining, Statistical Learning, Reinforcement Learning, Pattern Mining, Graph Mining, Relational Mining, Evolutionary Computation, Information Theory, Behavior Cloning, and many others. Topics 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 entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. Machine learning and data mining techniques have countless applications, including data science applications, and this reference is essential for anyone seeking quick access to vital information on the topic.
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Keywords
- Active learning
- big data
- clustering
- computational learning theory
- data mining
- data visualisation
- graph mining
- inductive logic programming
- information theory
- machine learning in bioinformatics
- meta-learning
- mining social data
- online learning
- pattern discovery
- recommender systems
- reinforcement learning
- sequential pattern analysis
- statistical language learning
- statistical maching learning
- text mining
Table of contents (950 entries)
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Front Matter
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Reviews
“The topics covered in the revised edition include applications, data mining, evolutionary computation, graph mining, information theory, learning and logic, pattern mining, reinforcement learning, relational mining, statistical learning, and text mining. … I recommend the encyclopedia as a valuable resource for libraries … .” (S. V. Nagaraj, Computing Reviews, January, 2018)
Editors and Affiliations
About the editors
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.
Bibliographic Information
Book Title: Encyclopedia of Machine Learning and Data Mining
Editors: Claude Sammut, Geoffrey I. Webb
DOI: https://doi.org/10.1007/978-1-4899-7687-1
Publisher: Springer New York, NY
eBook Packages: Computer Science, Reference Module Computer Science and Engineering
Copyright Information: Springer Science+Business Media New York 2017
Hardcover ISBN: 978-1-4899-7685-7Published: 15 March 2017
eBook ISBN: 978-1-4899-7687-1Published: 13 April 2017
Edition Number: 2
Number of Pages: XVII, 1335
Number of Illustrations: 180 b/w illustrations, 83 illustrations in colour
Topics: Artificial Intelligence, Data Mining and Knowledge Discovery, Statistics and Computing/Statistics Programs, Pattern Recognition