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Knowledge Representation and Organization in Machine Learning

  • Book
  • © 1989

Overview

Editors:
  1. Katharina Morik
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 347)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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Table of contents (15 chapters)

  1. Front Matter

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  2. Explanation: A source of guidance for knowledge representation

    • William R. Swartout, Stephen W. Smoliar
    Pages 1-16

  3. (Re)presentation issues in second generation expert systems

    • Walter Van de Velde
    Pages 17-49

  4. Some aspects of learning and reorganization in an analogical representation

    • Michael Mohnhaupt, Bernd Neumann
    Pages 50-64

  5. A knowledge-intensive learning system for document retrieval

    • Roy Rada, Hafedh Mili
    Pages 65-87

  6. Constructing expert systems as building mental models or toward a cognitive ontology for expert systems

    • David Littman
    Pages 88-106

  7. Sloppy modeling

    • Katharina Morik
    Pages 107-134

  8. The central role of explanations in disciple

    • Yves Kodratoff, Gheorghe Tecuci
    Pages 135-147

  9. An inference engine for representing multiple theories

    • Werner Emde
    Pages 148-176

  10. The acquisition of model-knowledge for a model-driven machine learning approach

    • Sabine Thieme
    Pages 177-191

  11. Using attribute dependencies for rule learning

    • Maarten W. van Someren
    Pages 192-210

  12. Learning disjunctive concepts

    • Michel Manago, Jim Blythe
    Pages 211-230

  13. The use of analogy in incremental SBL

    • Christel Vrain, Yves Kodratoff
    Pages 231-246

  14. Knowledge base refinement using apprenticeship learning techniques

    • David C. Wilkins
    Pages 247-257

  15. Creating high level knowledge structures from simple elements

    • Michael Pazzani
    Pages 258-288

  16. Demand-driven concept formation

    • Stefan Wrobel
    Pages 289-319
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Keywords

About this book

Machine learning has become a rapidly growing field of Artificial Intelligence. Since the First International Workshop on Machine Learning in 1980, the number of scientists working in the field has been increasing steadily. This situation allows for specialization within the field. There are two types of specialization: on subfields or, orthogonal to them, on special subjects of interest. This book follows the thematic orientation. It contains research papers, each of which throws light upon the relation between knowledge representation, knowledge acquisition and machine learning from a different angle. Building up appropriate representations is considered to be the main concern of knowledge acquisition for knowledge-based systems throughout the book. Here machine learning is presented as a tool for building up such representations. But machine learning itself also states new representational problems. This book gives an easy-to-understand insight into a new field with its problems and the solutions it offers. Thus it will be of good use to both experts and newcomers to the subject.

Bibliographic Information

  • Book Title: Knowledge Representation and Organization in Machine Learning

  • Editors: Katharina Morik

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/BFb0017213

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 1989

  • Softcover ISBN: 978-3-540-50768-0Published: 25 January 1989

  • eBook ISBN: 978-3-540-46081-7Published: 23 November 2005

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XVIII, 322

  • Topics: Artificial Intelligence

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