Learning in Non Stationary Environments

Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field.

Author: Moamar Sayed-Mouchaweh

Publisher: Springer

ISBN: 1489993401

Category: Computers

Page: 440

View: 450

Download →

Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences. Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy. Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations. This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research.

Related Books

Learning in Non-Stationary Environments
Language: en
Pages: 440
Authors: Moamar Sayed-Mouchaweh, Edwin Lughofer
Categories: Computers
Type: BOOK - Published: 2014-05-08 - Publisher: Springer

Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification
Machine Learning in Non-stationary Environments
Language: en
Pages: 261
Authors: Masashi Sugiyama, Motoaki Kawanabe
Categories: Computers
Type: BOOK - Published: 2012 - Publisher: MIT Press

Theory, algorithms, and applications of machine learning techniques to overcome "covariate shift" non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data
Learning in Non-Stationary Environments
Language: en
Pages: 454
Authors: Springer
Categories: Computers
Type: BOOK - Published: 2012-04-01 - Publisher:

Books about Learning in Non-Stationary Environments
Machine Learning in Non-Stationary Environments
Language: en
Pages: 279
Authors: Motoaki Kawanabe
Categories: Computers
Type: BOOK - Published: - Publisher:

Theory, algorithms, and applications of machine learning techniques to overcome "covariate shift" non-stationarity.
Learning in Non-Stationary Environments
Language: en
Pages: 440
Authors: Moamar Sayed-Mouchaweh, Edwin Lughofer
Categories: Computers
Type: BOOK - Published: 2012-04-13 - Publisher: Springer

Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification