Multistrategy Learning

Multistrategy Learning : A Special Issue of MACHINE LEARNING

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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.
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Product details

  • Hardback | 155 pages
  • 165.6 x 243.8 x 15.7mm | 430.92g
  • Dordrecht, Netherlands
  • English
  • Reprinted from MACHINE LEARNING, 11:2-3, 1993
  • IV, 155 p.
  • 0792393740
  • 9780792393740

Table of contents

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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