Multistrategy Learning

A Special Issue of MACHINE LEARNING

155 Seiten, Taschenbuch

Kurzbeschreibung des Verlags

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 m
ultistrategy 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.

• Reihe: The Springer International Series in Engineering and Computer Science
• ISBN: 9781461364054
• Sprache: Englisch
• Erscheinungsdatum: 08.10.2012
• Genre: Informatik, EDV/Informatik
• Verlag: Springer US
• Herausgegeben von: Ryszard S. Michalski