Multistrategy Learning : A Special Issue of MACHINE LEARNING

Ryszard S. Michalski

ISBN 10: 1461364051 / ISBN 13: 9781461364054

Editore: Springer US, 2012

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Druck auf Anfrage Neuware - Printed after ordering - 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 9781461364054

Dati bibliografici

• Titolo: Multistrategy Learning : A Special Issue of MACHINE LEARNING
• Editore: Springer US
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 1461364051
• ISBN 13: 9781461364054
• Rilegatura: Taschenbuch
• Condizione: Neu

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Riassunto

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.

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.