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.

zlib/no-category/Michalski, Ryszard S/Multistrategy Learning ||_73415613.pdf

edited by Ryszard S. Michalski

The Springer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems -- 240, Springer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems -- 240., Boston, MA, Massachusetts, 1993

The Kluwer International Series in Engineering and Computer Science, Boston, MA, 1993

United States, United States of America

Springer Nature, New York, NY, 2012

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2015-08-03