Learning from Data Streams in Dynamic Environments

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.

Examines the problem of learning in non-stationary environments, its interests, its applications and challenges Compares methods and techniques in order to show their complementarities and to define some new research directions in the area of learning in non-stationary environments Includes supplementary material: sn.pub/extras

Autorentext
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

Inhalt
Introduction to learning.- Learning in dynamic environment.- Handling concept drift.- Summary and final comments.