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Multistrategy Learning : The Springer International Series in Engineering and Computer Science - Ryszard S. Michalski

Multistrategy Learning

The Springer International Series in Engineering and Computer Science

By: Ryszard S. Michalski (Editor)

Hardcover

Published: 28th June 1993
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Most machine learning research has been concerned with the development of systems that implement 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 the learning problems they are applied to 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 that 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. It is an edited volume of original research comprising invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 11, Nos. 2/3).

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

ISBN: 9780792393740
ISBN-10: 0792393740
Series: The Springer International Series in Engineering and Computer Science
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 155
Published: 28th June 1993
Publisher: Springer
Country of Publication: NL
Dimensions (cm): 23.5 x 15.5  x 1.27
Weight (kg): 0.42