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Multistrategy Learning


Springer US,
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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.


Titel: Multistrategy Learning
Autoren/Herausgeber: Ryszard S. Michalski (Hrsg.)
Aus der Reihe: The Springer International Series in Engineering and Computer Science
Ausgabe: Reprinted from MACHINE LEARNING, 11:2-3, 1993

ISBN/EAN: 9780792393740

Seitenzahl: 155
Format: 23,5 x 15,5 cm
Produktform: Hardcover/Gebunden
Gewicht: 417 g
Sprache: Englisch - Newsletter
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