Table of contents

  1. Front Matter
  2. Stephen José Hanson, Werner Remmele, Ronald L. Rivest
    Pages 1-4
  3. Ronald L. Rivest
    Pages 5-7
  4. Avrim L. Blum, Ronald L. Rivest
    Pages 9-28
  5. Michael J. Kearns, Leslie G. Valiant
    Pages 29-49
  6. Ronald L. Rivest, Robert E. Schapire
    Pages 51-73
  7. Werner Remmele
    Pages 75-77
  8. R. Meunier, R. Scheiterer, A. Hecht
    Pages 93-105
  9. Robert W. Schwanke, Michael A. Platoff
    Pages 107-123
  10. Gary L. Drescher
    Pages 125-138
  11. Stephen Josè Hanson
    Pages 153-156
  12. Richard H. Lathrop, Teresa A. Webster, Temple F. Smith, Patrick H. Winston
    Pages 157-173
  13. Raymond L. Watrous
    Pages 203-227
  14. R. Chou, P. Liu, J. Vallino, M. Y. Chiu
    Pages 229-240
  15. Back Matter

About this book

Introduction

This volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens during a three-year joint research effort. It includes papers on many different styles of machine learning, organized into three parts. Part I, theory, includes three papers on theoretical aspects of machine learning. The first two use the theory of computational complexity to derive some fundamental limits on what isefficiently learnable. The third provides an efficient algorithm for identifying finite automata. Part II, artificial intelligence and symbolic learning methods, includes five papers giving an overview of the state of the art and future developments in the field of machine learning, a subfield of artificial intelligence dealing with automated knowledge acquisition and knowledge revision. Part III, neural and collective computation, includes five papers sampling the theoretical diversity and trends in the vigorous new research field of neural networks: massively parallel symbolic induction, task decomposition through competition, phoneme discrimination, behavior-based learning, and self-repairing neural networks.

Keywords

Automat algorithm algorithms artificial intelligence automata complexity intelligence learning machine learning

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-56483-7
  • Copyright Information Springer-Verlag 1993
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-56483-6
  • Online ISBN 978-3-540-47568-2
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book