Multistrategy Learning

A Special Issue of MACHINE LEARNING
Erscheinungsjahr 2012
ISBN: 978-1-4613-6405-4
Verlag: Springer

Buch, Englisch, 155 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 260 g

Reihe: The Springer International Series in Engineering and Computer Science

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 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 , 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.
contains contributions characteristic of the current research in this area.

Zielgruppe

Research

Autoren/Hrsg.

Michalski, Ryszard S.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

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.