Lazy Learning

  • David W. Aha

Table of contents

  1. Front Matter
    Pages i-6
  2. David W. Aha
    Pages 7-10
  3. Christopher G. Atkeson, Andrew W. Moore, Stefan Schaal
    Pages 11-73
  4. Christopher G. Atkeson, Andrew W. Moore, Stefan Schaal
    Pages 75-113
  5. Kai Ming Ting
    Pages 157-174
  6. Jianping Zhang, Yee-Sat Yim, Junming Yang
    Pages 175-191
  7. Oded Maron, Andrew W. Moore
    Pages 193-225
  8. Pat Langley, Karl Pfleger, Mehran Sahami
    Pages 315-342
  9. John W. Sheppard, Steven L. Salzberg
    Pages 343-370
  10. Walter Daelemans, Antal Van Den Bosch, Ton Weijters
    Pages 407-423

About this book

Introduction

This edited collection describes recent progress on lazy learning, a branch of machine learning concerning algorithms that defer the processing of their inputs, reply to information requests by combining stored data, and typically discard constructed replies. It is the first edited volume in AI on this topic, whose many synonyms include `instance-based', `memory-based'. `exemplar-based', and `local learning', and whose topic intersects case-based reasoning and edited k-nearest neighbor classifiers. It is intended for AI researchers and students interested in pursuing recent progress in this branch of machine learning, but, due to the breadth of its contributions, it should also interest researchers and practitioners of data mining, case-based reasoning, statistics, and pattern recognition.

Keywords

algorithms case-based reasoning classification cognition data mining learning machine learning

Editors and affiliations

  • David W. Aha
    • 1
  1. 1.Navy Center for Applied Research in Artificial IntelligenceNaval Research LaboratoryUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-017-2053-3
  • Copyright Information Springer Science+Business Media B.V. 1997
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-90-481-4860-8
  • Online ISBN 978-94-017-2053-3
  • About this book
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