Multistrategy Learning: A Special Issue of MACHINE LEARNING / Edition 1

by Ryszard S. Michalski

Overview

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.

Product Details

ISBN-13:	9780792393740
Publisher:	Springer US
Publication date:	06/30/1993
Series:	The Springer International Series in Engineering and Computer Science , #240
Edition description:	Reprinted from MACHINE LEARNING, 11:2-3, 1993
Pages:	155
Product dimensions:	6.10(w) x 9.25(h) x 0.24(d)

Table of Contents

Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning.- Multistrategy Learning and Theory Revision.- Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning.- Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding.- Balanced Cooperative Modeling.- Plausible Justification Trees: A Framework for Deep and Dynamic Integration of Learning Strategies.

Editorial Reviews

In six invited contributions, researchers report their successes and failures in developing machine learning systems that integrate two or more inference types and/or paradigms within one learning system, and so take advantage of the complementarity of different inference types or representational mechanisms. Such systems are more versatile and powerful than monostrategy systems, but are also much more complex and difficult to develop. Very slim index. Also published as Machine Learning vol. 11, nos. 2/3 (1993). Annotation c. Book News, Inc., Portland, OR (booknews.com)

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