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  • Reference work
  • © 2017

Encyclopedia of Machine Learning and Data Mining

Editors:

(view affiliations)
  • 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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Price excludes VAT (USA)
  • ISBN: 978-1-4899-7687-1
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Table of contents (950 entries)

  1. Front Matter

    Pages i-xvii
  2. A

    1. A/B Testing

      Pages 1-1
    2. Abduction

      • Antonis C. Kakas
      Pages 1-8
    3. Accuracy

      Pages 8-8
    4. ACO

      Pages 8-8
    5. Actions

      Pages 9-9
    6. Active Learning

      • David Cohn
      Pages 9-14
    7. Active Learning Theory

      • Sanjoy Dasgupta
      Pages 14-19
    8. Adaboost

      Pages 19-20
    9. Adaptive Learning

      Pages 20-20
    10. Adaptive Real-Time Dynamic Programming

      • Andrew G. Barto
      Pages 20-23
    11. Adaptive Resonance Theory

      • Gail A. Carpenter, Stephen Grossberg
      Pages 24-40
    12. Adaptive System

      Pages 40-40
    13. Agent

      Pages 40-40
    14. AIS

      Pages 40-40

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.


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

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

  • The University of New South Wales, Sydney, Australia

    Claude Sammut

  • Faculty of Information Technology, Monash University, Melbourne, Australia

    Geoffrey I. Webb

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

Buying options

eBook
USD 799.99
Price excludes VAT (USA)
  • ISBN: 978-1-4899-7687-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD 899.99
Price excludes VAT (USA)