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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
An Information-Theoretic Approach to Neural Computing

An Information-Theoretic Approach to Neural Computing

Obradovic, Dragan
Deco, Gustavo

Paperback

Series: Perspectives in Neural Computing

General Computers

ISBN10: 1461284694
ISBN13: 9781461284697
Publisher: Springer Nature
Published: Sep 17 2011
Pages: 262
Weight: 0.88
Height: 0.59 Width: 6.14 Depth: 9.21
Language: English
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 non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all 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 to be a very valuable introduction to this topic.

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