An information-theoretic approach to neural computing
By: Gustavo Deco, Dragan Obradovic.
Contributors: Obradovic, Dragan.
Series: Perspectives in neural computing
Publisher: Springer
Published: 1996
Place of publication: New York
Language: eng
Number of pages: 261
Subjects: Neural networks (Computer science)
Description: Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint. They show how this perspective provides new insights into the design theory of neural networks.

In particular, they show how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and nonlinear independent component analysis, and Boltzmann machines.

Readers are assumed to have a basic understanding of neural networks, but all of the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from several different scientific disciplines - notably, cognitive scientists, engineers, physicists, statisticians, and computer scientists - will find this book to be a very valuable contribution to this topic.
Notes: Includes bibliographical references (p. 243-257)and index.