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Lazy Learning

  • Book
  • © 1997

Overview

Editors:
  1. David W. Aha

    1. Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, USA

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Table of contents (15 chapters)

  1. Front Matter

    Pages i-6
    Download chapter PDF

  2. Editorial

    • David W. Aha
    Pages 7-10

  3. Locally Weighted Learning

    • Christopher G. Atkeson, Andrew W. Moore, Stefan Schaal
    Pages 11-73

  4. Locally Weighted Learning for Control

    • Christopher G. Atkeson, Andrew W. Moore, Stefan Schaal
    Pages 75-113

  5. Voting over Multiple Condensed Nearest Neighbors

    • Ethem Alpaydin
    Pages 115-132

  6. Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching

    • Marcos Salganicoff
    Pages 133-155

  7. Discretisation in Lazy Learning Algorithms

    • Kai Ming Ting
    Pages 157-174

  8. Intelligent Selection of Instances for Prediction Functions in Lazy Learning Algorithms

    • Jianping Zhang, Yee-Sat Yim, Junming Yang
    Pages 175-191

  9. The Racing Algorithm: Model Selection for Lazy Learners

    • Oded Maron, Andrew W. Moore
    Pages 193-225

  10. Context-Sensitive Feature Selection for Lazy Learners

    • Pedro Domingos
    Pages 227-253

  11. Computing Optimal Attribute Weight Settings for Nearest Neighbor Algorithms

    • Charles X. Ling, Handong Wang
    Pages 255-272

  12. A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms

    • Dietrich Wettschereck, David W. Aha, Takao Mohri
    Pages 273-314

  13. Lazy Acquisition of Place Knowledge

    • Pat Langley, Karl Pfleger, Mehran Sahami
    Pages 315-342

  14. A Teaching Strategy for Memory-Based Control

    • John W. Sheppard, Steven L. Salzberg
    Pages 343-370

  15. Lazy Incremental Learning of Control Knowledge for Efficiently Obtaining Quality Plans

    • Daniel Borrajo, Manuela Veloso
    Pages 371-405

  16. IGTree: Using Trees for Compression and Classification in Lazy Learning Algorithms

    • Walter Daelemans, Antal Van Den Bosch, Ton Weijters
    Pages 407-423
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Keywords

About this book

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.

Editors and Affiliations

  • Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, USA

    David W. Aha

Bibliographic Information

  • Book Title: Lazy Learning

  • Editors: David W. Aha

  • DOI: https://doi.org/10.1007/978-94-017-2053-3

  • Publisher: Springer Dordrecht

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media Dordrecht 1997

  • Hardcover ISBN: 978-0-7923-4584-8Published: 31 May 1997

  • Softcover ISBN: 978-90-481-4860-8Published: 01 December 2010

  • eBook ISBN: 978-94-017-2053-3Published: 29 June 2013

  • Edition Number: 1

  • Number of Pages: IV, 424

  • Topics: Artificial Intelligence

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