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

List price: £206.50


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ISBN 10: 1461364051
ISBN 13: 9781461364054
Publisher: SPRINGER-VERLAG NEW YORK INC.
Pub. date: 8 October, 2012
Edition: Softcover reprint of the original 1st ed. 1993
Series: The Springer International Series in Engineering and Computer Science 240
Pages: 155
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: US
Imprint: Springer-Verlag New York Inc.
Returns: Returnable
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