Multistrategy Learning : A Special Issue of Machine Learning

Ryszard S Michalski

ISBN 10: 0792393740 / ISBN 13: 9780792393740

Edité par Springer Us Jun 1993, 1993

A propos de cet article

Neuware - 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.

N° de réf. du vendeur 9780792393740

Détails bibliographiques

• Titre: Multistrategy Learning : A Special Issue of Machine Learning
• Éditeur: Springer Us Jun 1993
• Date de parution: 1993
• Langue: anglais
• ISBN à 10 chiffres: 0792393740
• ISBN à 13 chiffres: 9780792393740
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 244 L x 166 H x 16 P (en millimètres) 244 millimètres de largeur par 166 millimètres de hauteur par 16 millimètres de profondeur
• Poids: 431 grammes

A propos de ce titre

Synopsis

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.