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.
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