Edited By
Ryszard Michalski, George Mason University
George Tecuci
Description
Multistrategy learning is one of the newest and most promising research directions in the development of machine learning systems.
The objectives of research in this area are to study trade-offs between different learning strategies and to develop learning systems
that employ multiple types of inference or computational paradigms in a learning process. Multistrategy systems offer significant advantages
over monostrategy systems. They are more flexible in the type of input they can learn from and the type of knowledge they can acquire.
As a consequence, multistrategy systems have the potential to be applicable to a wide range of practical problems. This volume is the
first book in this fast growing field. It contains a selection of contributions by leading researchers specializing in this area.
