Multistrategy Learning : A Special Issue of Machine Learning

Ryszard S Michalski

ISBN 10: 0792393740 / ISBN 13: 9780792393740

Editore: Springer Us Jun 1993, 1993

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Neuware - 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.

Codice articolo 9780792393740

Dati bibliografici

• Titolo: Multistrategy Learning : A Special Issue of Machine Learning
• Editore: Springer Us Jun 1993
• Data di pubblicazione: 1993
• Lingua: Inglese
• ISBN 10: 0792393740
• ISBN 13: 9780792393740
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 244 L x 166 A x 16 P (millimetri) 244 millimetri di larghezza per 166 millimetri di altezza per 16 millimetri di profondità
• Peso: 431 grammi

Descrizione articolo

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).

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.