Learning in Non-Stationary Environments : Methods and Applications

Edwin Lughofer

ISBN 10: 1441980199 / ISBN 13: 9781441980199

Edité par Springer New York, Springer US, 2012

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9781441980199

Détails bibliographiques

• Titre: Learning in Non-Stationary Environments : Methods and Applications
• Éditeur: Springer New York, Springer US
• Date de parution: 2012
• Langue: anglais
• ISBN à 10 chiffres: 1441980199
• ISBN à 13 chiffres: 9781441980199
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 241 L x 160 H x 28 P (en millimètres) 241 millimètres de largeur par 160 millimètres de hauteur par 28 millimètres de profondeur
• Poids: 840 grammes

A propos de ce titre

Synopsis

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.

À propos de l?auteur

Both editors are active researchers in the area with a significant publication record where the publications themselves are in the general area to be covered in the proposed edited volume.