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

  • Haym Hirsh

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

Table of contents

  1. Front Matter
    Pages i-xv
  2. Haym Hirsh
    Pages 1-7
  3. Haym Hirsh
    Pages 9-14
  4. Haym Hirsh
    Pages 69-74
  5. Haym Hirsh
    Pages 75-82
  6. Haym Hirsh
    Pages 83-95
  7. Haym Hirsh
    Pages 97-101
  8. Back Matter
    Pages 103-115

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.


Extension algorithms complexity formal proof knowledge learning machine learning

Authors and affiliations

  • Haym Hirsh
    • 1
  1. 1.Rutgers UniversityUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag US 1990
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-8834-3
  • Online ISBN 978-1-4613-1557-5
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site