Ryszard S. Michalski (Hrsg.)

Multistrategy Learning

A Special Issue of Machine Learning
Herausgegeben:Michalski, Ryszard S.

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• The Springer International Series in Engineering and Computer Science 240
• Verlag: Springer / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 978-0-7923-9374-0
• 1993.
• Seitenzahl: 155
• Erscheinungstermin: 30. Juni 1993
• Englisch
• Abmessung: 244mm x 166mm x 16mm
• Gewicht: 417g
• ISBN-13: 9780792393740
• ISBN-10: 0792393740
• Artikelnr.: 22048187

Inhaltsangabe

Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning.- Multistrategy Learning and Theory Revision.- Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning.- Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding.- Balanced Cooperative Modeling.- Plausible Justification Trees: A Framework for Deep and Dynamic Integration of Learning Strategies.