Multistrategy Learning: A Special Issue of MACHINE LEARNING: 240 - Rilegato

Michalski, Ryszard S.

Rilegato

ISBN 10: 0792393740 ISBN 13: 9780792393740

Casa editrice: Kluwer Academic Pub, 1993

Riassunto

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).

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Contenuti

Introduction; R.S. Michalski. Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning; R.S. Michalski. Multistrategy Learning and Theory Revision; L. Saitta, M. Botta, F. Neri. Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning; M. Pazzani. Using Knowledge-Based Neural Networks to Improve Algorithms: refining the Chou--Fasman Algorithm for Protein Folding; R. Maclin, J.W. Shavlik. Balanced Cooperative Modeling; K. Morik. Plausible Justification Trees: a Framework for Deep and Dynamic Integration of Learning Strategies; G. Tecuci. Index.

Product Description

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Dettagli dell'edizione

• Editore: Kluwer Academic Pub
• Data di pubblicazione: 1993
• Lingua: Inglese
• ISBN 10: 0792393740
• ISBN 13: 9780792393740
• Rilegatura: Copertina rigida
• Numero edizione: 2
• Numero di pagine: 155
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania