Self-Organizing Neural Networks | Recent Advances and Applications

Udo Seiffert

ISBN 10: 3662003430 / ISBN 13: 9783662003435

Published by Physica, 2014

About this Item

Self-Organizing Neural Networks | Recent Advances and Applications | Udo Seiffert | Taschenbuch | xiv | Englisch | 2014 | Physica | EAN 9783662003435 | Verantwortliche Person für die EU: Physica Verlag in Springer Science + Business Media, Tiergartenstr. 15-17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 105278547

Bibliographic Details

• Title: Self-Organizing Neural Networks | Recent Advances and Applications
• Publisher: Physica
• Publication Date: 2014
• Language: English
• ISBN 10: 3662003430
• ISBN 13: 9783662003435
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 17D millimeters 235 millimeters width by 155 millimeters height by 17 millimeters depth
• Weight: 452 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

The Self-Organizing Map (SOM) is one of the most frequently used architectures for unsupervised artificial neural networks. Introduced by Teuvo Kohonen in the 1980s, SOMs have been developed as a very powerful method for visualization and unsupervised classification tasks by an active and innovative community of interna­ tional researchers. A number of extensions and modifications have been developed during the last two decades. The reason is surely not that the original algorithm was imperfect or inad­ equate. It is rather the universal applicability and easy handling of the SOM. Com­ pared to many other network paradigms, only a few parameters need to be arranged and thus also for a beginner the network leads to useful and reliable results. Never­ theless there is scope for improvements and sophisticated new developments as this book impressively demonstrates. The number of published applications utilizing the SOM appears to be unending. As the title of this book indicates, the reader will benefit from some of the latest the­ oretical developments and will become acquainted with a number of challenging real-world applications. Our aim in producing this book has been to provide an up­ to-date treatment of the field of self-organizing neural networks, which will be ac­ cessible to researchers, practitioners and graduated students from diverse disciplines in academics and industry. We are very grateful to the father of the SOMs, Professor Teuvo Kohonen for sup­ porting this book and contributing the first chapter.