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Item Details
Title: MULTISTRATEGY LEARNING
A SPECIAL ISSUE OF MACHINE LEARNING
By: Ryszard S. Michalski (Editor)
Format: Hardback

List price: £206.50


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ISBN 10: 0792393740
ISBN 13: 9780792393740
Publisher: SPRINGER
Pub. date: 30 June, 1993
Edition: Reprinted from MACHINE LEARNING, 11:2-3, 1993
Series: The Springer International Series in Engineering and Computer Science 240
Pages: 155
Description: Most machine learning research has been concerned with the development of systems that implememnt one type of inference within a single representational paradigm. This title contains contributions characteristic of the research in this area.
Synopsis: 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.
Illustrations: IV, 155 p.
Publication: Netherlands
Imprint: Springer
Returns: Returnable
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