Hebbian Learning And Negative Feedback Networks - Couverture rigide

Fyfe, Colin

Couverture rigide

ISBN 10 : 1852338830 ISBN 13 : 9781852338831

Editeur : Springer London Ltd, 2005

Synopsis

A state of the art specialist monograph on artificial neural networks which use Hebbian learning, covering a wide range of real experiments and which displays how it's approaches can be applied to analyse real problems. The book has a thorough approach and brings together a wide range of concepts into a coherent whole. Colin Fyfe writes with authority, and is a well-known, experienced researcher who has led a team working in this area at Paisley.

A propos du livre

Présentation de l'éditeur

The central idea of Hebbian Learning and Negative Feedback Networks is that artificial neural networks using negative feedback of activation can use simple Hebbian learning to self-organise so that they uncover interesting structures in data sets. Two variants are considered: the first uses a single stream of data to self-organise. By changing the learning rules for the network, it is shown how to perform Principal Component Analysis, Exploratory Projection Pursuit, Independent Component Analysis, Factor Analysis and a variety of topology preserving mappings for such data sets.

The second variants use two input data streams on which they self-organise. In their basic form, these networks are shown to perform Canonical Correlation Analysis, the statistical technique which finds those filters onto which projections of the two data streams have greatest correlation.

The book encompasses a wide range of real experiments and displays how the approaches it formulates can be applied to the analysis of real problems.

Détails de l'édition

• Éditeur: Springer London Ltd
• Date d'édition: 2005
• Langue: anglais
• ISBN 10: 1852338830
• ISBN 13: 9781852338831
• Reliure: Relié
• Nombre de pages: 401
• Coordonnées du fabricant: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Allemagne