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Multistrategy Learning: A Special Issue of MACHINE LEARNING

Multistrategy Learning: A Special Issue of MACHINE LEARNING (Hardback)

£206.50
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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.

Computing & ITComputer scienceArtificial intelligenceMachine learningReference & ResearchLibrary & information sciencesInformation theoryCybernetics & systems theoryReference & ResearchResearch & information: general Publisher: Springer Publication Date: 30/06/1993 ISBN-13: 9780792393740  Details: Type: Hardback Format: Books
Availability: Printed to order. Despatched in 2-3 weeks.  

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