Overview

Theory of Neural Information Processing Systems provides an explicit, coherent, and up-to-date account of the modern theory of neural information processing systems. It has been carefully developed for graduate students from any quantitative discipline, including mathematics, computer science, physics, engineering or biology, and has been thoroughly class-tested by the authors over a period of some 8 years. Exercises are presented throughout the text and notes on historical background and further reading guide the student into the literature. All mathematical details are included and appendices provide further background material, including probability theory, linear algebra and stochastic processes, making this textbook accessible to a wide audience.

Full Product Details

Author:   A.C.C. Coolen ,  R. Kuehn ,  P. Sollich
Publisher:   Oxford University Press
Imprint:   Oxford University Press
Dimensions:   Width: 17.20cm , Height: 3.30cm , Length: 24.50cm
Weight:   1.021kg
ISBN:  

9780198530244

ISBN 10:   0198530242
Pages:   586
Publication Date:   21 July 2005
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Paperback
Publisher's Status:   Active
Availability:   To order  
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Table of Contents

I Introduction to Neural Networks 1: General introduction 2: Layered networks 3: Recurrent networks with binary neurons II Advanced Neural Networks 4: Competitive unsupervised learning processes 5: Bayesian techniques in supervised learning 6: Gaussian processes 7: Support vector machines for binary classification III Information Theory and Neural Networks 8: Measuring information 9: Identification of entropy as an information measure 10: Building blocks of Shannon's information theory 11: Information theory and statistical inference 12: Applications to neural networks IV Macroscopic Analysis of Dynamics 13: Network operation: macroscopic dynamics 14: Dynamics of online learning in binary perceptrons 15: Dynamics of online gradient descent learning V Equilibrium Statistical Mechanics of Neural Networks 16: Basics of equilibrium statistical mechanics 17: Network operation: equilibrium analysis 18: Gardner theory of task realizability Appendices A: Historical and bibliographical notes B: Probability theory in a nutshell C: Conditions for central limit theorem to apply D: Some simple summation identities E: Gaussian integrals and probability distributions F: Matrix identities G: The delta-distribution H: Inequalities based on convexity I: Metrics for parametrized probability distributions J: Saddle-point integration References

Reviews

The book provides an excellent class-tested material for graduate courses in artificial neural networks. It is completely self-contained and includes also thorough introduction to the discussed discipline-specific areas of mathematics...Therefore, this book represents a good reference source of applicable ideas for a wide audience including students, researchers and application specialists as well. * EMS Newsletter *

The book provides an excellent class-tested material for graduate courses in artificial neural networks. It is completely self-contained and includes also thorough introduction to the discussed discipline-specific areas of mathematics...Therefore, this book represents a good reference source of applicable ideas for a wide audience including students, researchers and application specialists as well. EMS Newsletter

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