Advertisement

Innovations in Machine Learning

Theory and Applications

  • Dawn E. Holmes
  • Lakhmi C. Jain

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 194)

Table of contents

  1. Front Matter
    Pages I-XVI
  2. David Heckerman, Christopher Meek, Gregory Cooper
    Pages 1-28
  3. Richard E. Neapolitan, Xia Jiang
    Pages 29-71
  4. Dan Roth
    Pages 73-95
  5. Frederick Eberhardt, Clark Glymour, Richard Scheines
    Pages 97-112
  6. S. H. Muggleton, H. Lodhi, A. Amini, M. J. E. Sternberg
    Pages 113-135
  7. Yoshua Bengio, Holger Schwenk, Jean-Sébastien Senécal, Fréderic Morin, Jean-Luc Gauvain
    Pages 137-186
  8. Pieter W. Adriaans, Menno M. van Zaanen
    Pages 187-203
  9. Nello Cristianini, Jaz Kandola, Andre Elisseeff, John Shawe-Taylor
    Pages 205-256
  10. Ralf Herbrich, Thore Graepel, Robert C. Williamson
    Pages 257-273
  11. Back Matter
    Pages 275-276

About this book

Introduction

Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained.

Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Postgraduate since it shows the direction of current research.

Keywords

Case-based Machine Learning Paradigms Computational Algorithms in Machine Learning Explanation-based Learning Paradigms algorithm algorithms artificial intelligence genetic algorithms intelligence kernel learning logic programming machine learning neural networks programming

Editors and affiliations

  • Dawn E. Holmes
    • 1
  • Lakhmi C. Jain
    • 2
  1. 1.Department of Statistics and Applied ProbabilityUniversity of California at Santa Barbara, South HallSanta BarbaraUSA
  2. 2.School of Electrical & Information EngineeringKnowledge-Based Intelligent EngineeringAdelaideAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-33486-6
  • Copyright Information Springer-Verlag Berlin Heidelberg 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-30609-2
  • Online ISBN 978-3-540-33486-6
  • Series Print ISSN 1434-9922
  • Buy this book on publisher's site
Industry Sectors
Materials & Steel
Automotive
Chemical Manufacturing
Biotechnology
Electronics
IT & Software
Telecommunications
Consumer Packaged Goods
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences
Engineering