Learning from Data Streams in Dynamic Environments

1. Front Matter

   Pages i-viii

   PDF

2. Introduction to Learning

   • Moamar Sayed-Mouchaweh

   Pages 1-10

3. Learning in Dynamic Environments

   • Moamar Sayed-Mouchaweh

   Pages 11-32

4. Handling Concept Drift

   • Moamar Sayed-Mouchaweh

   Pages 33-59

5. Summary and Final Comments

   • Moamar Sayed-Mouchaweh

   Pages 61-69

6. Back Matter

   Pages 71-75

   PDF

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.

• Adaptive Modeling
• Data Streams
• Drift Monitoring and Handling
• Evolving Systems
• Incremental Learning
• Non-stationary Environments
• Online Learning

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

  Moamar Sayed-Mouchaweh

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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