This book addresses the problems of modeling, prediction, classification, data understanding and processing in non-stationary and unpredictable environments. It presents major and well-known methods and approaches for the design of systems able to learn and to fully adapt its structure and to adjust its parameters according to the changes in their environments. Also presents the problem of learning in non-stationary environments, its interests, its applications and challenges and studies the complementarities and the links between the different methods and techniques of learning in evolving and non-stationary environments.
表中的内容
Introduction to learning.- Learning in dynamic environment.- Handling concept drift.- Summary and final comments.
关于作者
Moamar Sayed-Mouchaweh is a Professor at High National Engineering School of Mines “Ecole Nationale Supérieure des Mines de Douai”, Computer Science and Automatic Labs, Douai-France
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