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Learning from Data Streams in Dynamic Environments

  • Book
  • © 2016

Overview

  • 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

Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)

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Table of contents (4 chapters)

Keywords

About this book

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.

Authors and Affiliations

  • Computer Science and Automatic Control, High National Engineering School of Mine Computer Science and Automatic Control, Douai, France

    Moamar Sayed-Mouchaweh

About the author

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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