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Incremental Version-Space Merging: A General Framework for Concept Learning

  • Book
  • © 1990

Overview

Authors:
  1. Haym Hirsh

    1. Rutgers University, USA

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Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 104)

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xv
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  2. Overview

    • Haym Hirsh
    Pages 1-7

  3. Incremental Version-Space Merging

    • Haym Hirsh
    Pages 9-14

  4. The Candidate-Elimination Algorithm: Emulation and Extensions

    • Haym Hirsh
    Pages 15-27

  5. Learning from Data with Bounded Inconsistency

    • Haym Hirsh
    Pages 29-48

  6. Combining Empirical and Analytical Learning

    • Haym Hirsh
    Pages 49-67

  7. Incremental Batch Learning

    • Haym Hirsh
    Pages 69-74

  8. Computational Complexity

    • Haym Hirsh
    Pages 75-82

  9. Theoretical Underpinnings

    • Haym Hirsh
    Pages 83-95

  10. Conclusions

    • Haym Hirsh
    Pages 97-101

  11. Back Matter

    Pages 103-115
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Keywords

About this book

One of the most enjoyable experiences in science is hearing a simple but novel idea which instantly rings true, and whose consequences then begin to unfold in unforeseen directions. For me, this book presents such an idea and several of its ramifications. This book is concerned with machine learning. It focuses on a ques­ tion that is central to understanding how computers might learn: "how can a computer acquire the definition of some general concept by abstracting from specific training instances of the concept?" Although this question of how to automatically generalize from examples has been considered by many researchers over several decades, it remains only partly answered. The approach developed in this book, based on Haym Hirsh's Ph.D. dis­ sertation, leads to an algorithm which efficiently and exhaustively searches a space of hypotheses (possible generalizations of the data) to find all maxi­ mally consistent hypotheses, even in the presence of certain types of incon­ sistencies in the data. More generally, it provides a framework for integrat­ ing different types of constraints (e.g., training examples, prior knowledge) which allow the learner to reduce the set of hypotheses under consideration.

Authors and Affiliations

  • Rutgers University, USA

    Haym Hirsh

Bibliographic Information

  • Book Title: Incremental Version-Space Merging: A General Framework for Concept Learning

  • Authors: Haym Hirsh

  • Series Title: The Springer International Series in Engineering and Computer Science

  • DOI: https://doi.org/10.1007/978-1-4613-1557-5

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Kluwer Academic Publishers 1990

  • Hardcover ISBN: 978-0-7923-9119-7Published: 31 July 1990

  • Softcover ISBN: 978-1-4612-8834-3Published: 26 September 2011

  • eBook ISBN: 978-1-4613-1557-5Published: 06 December 2012

  • Series ISSN: 0893-3405

  • Edition Number: 1

  • Number of Pages: XVI, 116

  • Topics: Artificial Intelligence

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