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Computational Models of Learning

  • LeonardĀ Bolc

Part of the Symbolic Computation book series (SYMBOLIC)

Table of contents

  1. Front Matter
    Pages I-IX
  2. Pat Langley, Herbert A. Simon, Gary L. Bradshaw
    Pages 21-54
  3. Larry Rendell
    Pages 89-159
  4. J. G. Wolff
    Pages 161-205
  5. Back Matter
    Pages 207-208

About this book

Introduction

In recent years, machine learning has emerged as a significant area of research in artificial intelligence and cognitive science. At present, research in the field is being intensified from both the point of view of theory and of implementation, and the results are being introduced in practice. Machine learning has recently become the subject of interest of many young and talented scientists whose bold ideas have greatly contributed to the broadening of knowledge in this rapidly developing field of science. This situation has manifested itself in an increasing number of valuable contributions to scientific journals. However, such papers are necessarily compact descriptions of research problems. Computational Models of Learning supplements these contributions and is a collection of more extensive essays. These essays provide the reader with an increased knowledge of carefully selected problems of machine learning.

Keywords

artificial intelligence heuristics intelligence knowledge learning machine learning optimization

Editors and affiliations

  • LeonardĀ Bolc
    • 1
  1. 1.Institute of InformaticsWarsaw UniversityWarszawaPoland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-82742-6
  • Copyright Information Springer-Verlag Berlin Heidelberg 1987
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-82744-0
  • Online ISBN 978-3-642-82742-6
  • Buy this book on publisher's site