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A guide to neural computing applications /

Neural networks have shown enormous potential for commercial exploitation over the last few years but it is easy to overestimate their capabilities. A few simple algorithms will learn relationships between cause and effect or organise large volumes of data into orderly and informative patterns but t...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Tarassenko, Lionel
Autor Corporativo: Neural Computing Applications Forum
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : New York : Arnold ; Co-published in North, Central, and South America by John Wiley, ©1998.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Tarassenko, Lionel. 
245 1 2 |a A guide to neural computing applications /  |c Lionel Tarassenko. 
260 |a London :  |b Arnold ;  |a New York :  |b Co-published in North, Central, and South America by John Wiley,  |c ©1998. 
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500 |a On t.p.: Neural Computing Applications Forum. 
500 |a "Commissioned for [Neural Computing Applications Forum] members"--Page ix 
504 |a Includes bibliographical references and index. 
588 0 |a Print version record. 
505 0 |a Front Cover; A Guide to Neural Computing Applications; Copyright Page; Contents; Foreword; Chapter 1. Introduction; Chapter 2. Mathematical background for neural computing; Chapter 3. Managing a neural computing project; Chapter 4. Identifying applications and assessing their feasibility; Chapter 5. Neural computing hardware and software; Chapter 6. Collecting and preparing data; Chapter 7. Design, training and testing of the prototype; Chapter 8. The case studies; Chapter 9. More advanced topics; Appendix A: The error back-propagation algorithm for weight updates in an MLP. 
520 |a Neural networks have shown enormous potential for commercial exploitation over the last few years but it is easy to overestimate their capabilities. A few simple algorithms will learn relationships between cause and effect or organise large volumes of data into orderly and informative patterns but they cannot solve every problem and consequently their application must be chosen carefully and appropriately. This book outlines how best to make use of neural networks. It enables newcomers to the technology to construct robust and meaningful non-linear models and classifiers and benefits the more. 
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