Multistrategy learning

著者

書誌事項

Multistrategy learning

edited by Ryszard S. Michalski

（The Kluwer international series in engineering and computer science, SECS 240 . Knowledge representation, learning, and expert systems）

Kluwer Academic, c1993

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注記

"A special issue of Machine learning."

"Reprinted from Machine learning, vol. 11, nos. 2-3 (1993)."

Includes bibliographical references and index

内容説明・目次

内容説明

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.

目次

  • 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.

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