Multistrategy Learning

By: Ryszard S. Michalski (Editor)

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Hardcover | 28 June 1993

At a Glance

Hardcover
164 Pages

Dimensions(cm)
23.39 x 15.6 x 1.12

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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.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Mathematical Theory of Computation Psychology Cognition & Cognitive Psychology Artificial Intelligence Machine Learning Reference, Information & Interdisciplinary Subjects Research & Information Information theory Cybernetics & Systems Theory

Introduction	p. 1
Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning	p. 3
Multistrategy Learning and Theory Revision	p. 45
Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning	p. 65
Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding	p. 87
Balanced Cooperative Modeling	p. 109
Plausible Justification Trees: A Framework for Deep and Dynamic Integration of Learning Strategies	p. 129
Table of Contents provided by Blackwell. All Rights Reserved.

Multistrategy Learning

At a Glance

Hardcover

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How to return your order

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