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2016 | Buch

Introduction to Learning Kapitel lesen Erstes Kapitel lesen

Learning from Data Streams in Dynamic Environments

verfasst von: Moamar Sayed-Mouchaweh

Verlag: Springer International Publishing

Buchreihe : SpringerBriefs in Applied Sciences and Technology

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction to Learning
Abstract
This chapter presents the basic definitions and notation related to the problem of learning from data samples. It shows how a learner (e.g., classifier) is built, and its performance is evaluated using multiple real and academic examples.
Moamar Sayed-Mouchaweh
Chapter 2. Learning in Dynamic Environments
Abstract
In this chapter, the problem of drifting data streams in dynamic environments is formalized, and its framework is defined. Then, the kinds and characteristics of the concept drift are presented. Finally, the real-world applications generating drifting data streams are discussed. The goal is to give a picture of the problem of learning from data streams in dynamic environments, its causes, sources, and characteristics in order to discuss later alternatives to solve this problem.
Moamar Sayed-Mouchaweh
Chapter 3. Handling Concept Drift
Abstract
In this chapter, the different methods and techniques used to learn from data streams in evolving and nonstationary environments will be presented, and their performances will be compared according to the generated drift characteristics as well as to the application context and objectives. The goal is to define the criteria to be used in order to help readers to efficiently design the suitable learning scheme for a particular application. For this aim, these methods and techniques are classified and compared according to a set of meaningful criteria. Several examples will be used to illustrate and discuss the principal and the performance of these methods and techniques.
Moamar Sayed-Mouchaweh
Chapter 4. Summary and Final Comments
Abstract
In this concluding chapter, the different chapters of this book will be summarized. Then, the future tendencies and not-yet-addressed challenges will be presented and discussed.
Moamar Sayed-Mouchaweh
Backmatter
Metadaten
Titel
Learning from Data Streams in Dynamic Environments
verfasst von
Moamar Sayed-Mouchaweh
Copyright-Jahr
2016
Electronic ISBN
978-3-319-25667-2
Print ISBN
978-3-319-25665-8
DOI
https://doi.org/10.1007/978-3-319-25667-2

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