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Multistrategy Learning

A Special Issue of MACHINE LEARNING

  • Ryszard S. Michalski

Table of contents

About this book

Introduction

Most machine learning research has been concerned with the development of systems that implememnt one type of inference within a single representational paradigm. Such systems, which can be called monostrategy learning systems, include those for empirical induction of decision trees or rules, explanation-based generalization, neural net learning from examples, genetic algorithm-based learning, and others. Monostrategy learning systems can be very effective and useful if learning problems to which they are applied are sufficiently narrowly defined.
Many real-world applications, however, pose learning problems that go beyond the capability of monostrategy learning methods. In view of this, recent years have witnessed a growing interest in developing multistrategy systems, which integrate two or more inference types and/or paradigms within one learning system. Such multistrategy systems take advantage of the complementarity of different inference types or representational mechanisms. Therefore, they have a potential to be more versatile and more powerful than monostrategy systems. On the other hand, due to their greater complexity, their development is significantly more difficult and represents a new great challenge to the machine learning community.
Multistrategy Learning contains contributions characteristic of the current research in this area.

Keywords

algorithms complexity decision tree genetic algorithms knowledge learning machine learning modeling networks neural networks system

Editors and affiliations

  • Ryszard S. Michalski
    • 1
  1. 1.George Mason UniversityUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-3202-6
  • Copyright Information Kluwer Academic Publishers 1993
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-6405-4
  • Online ISBN 978-1-4615-3202-6
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site