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Theory of neural information processing systems /
"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,...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Oxford :
Oxford University Press,
2005.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Coolen, A. C. C. |q (Anthony C. C.), |d 1960- | |
| 245 | 1 | 0 | |a Theory of neural information processing systems / |c A.C.C. Coolen, R. Kühn., P. Sollich. |
| 260 | |a Oxford : |b Oxford University Press, |c 2005. | ||
| 300 | |a 1 online resource (xvi, 569 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | 0 | |g Machine generated contents note: |g pt. I |t Introduction to neural networks -- |g 1. |t General introduction -- |g 2. |t Layered networks -- |g 3. |t Recurrent networks with binary neurons -- |g 4. |t Notes and suggestions for further reading -- |g pt. II |t Advanced neural networks -- |g 5. |t Competitive unsupervised learning processes -- |g 6. |t Bayesian techniques in supervised learning -- |g 7. |t Gaussian processes -- |g 8. |t Support vector machines for binary classification -- |g 9. |t Notes and suggestions for further reading -- |g pt. III |t Information theory and neural networks -- |g 10. |t Measuring information -- |g 11. |t Identification of entropy as an information measure -- |g 12. |t Building blocks of Shannon's information theory -- |g 13. |t Information theory and statistical inference -- |g 14. |t Applications to neural networks -- |g 15. |t Notes and suggestions for further reading -- |g pt. IV |t Macroscopic analysis of dynamics -- |g 16. |t Network operation : macroscopic dynamics -- |g 17. |t Dynamics of online learning in binary perceptions -- |g 18. |t Dynamics of online gradient descent learning -- |g 19. |t Notes and suggestions for further reading -- |g pt. V |t Equilibrium statistical mechanics of neural networks -- |g 20. |t Basics of equilibrium statistical mechanics -- |g 21. |t Network operation : equilibrium analysis -- |g 22. |t Gardner theory of task realizability -- |g 23. |t Notes and suggestions for further reading -- |g App. |t A Probability theory in a nutshell -- |g App. B |t Conditions for the central limit theorem to apply -- |g App. C |t Some simple summation identities -- |g App. D |t Gaussian integrals and probability distributions -- |g App. E |t Matrix identities -- |g App. F |t [delta]-distribution -- |g App. G |t Inequalities based on convexity -- |g App. H |t Metrics for parametrized probability distributions. |
| 520 | |a "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, 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 students 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."--Jacket. | ||
| 546 | |a English. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 7 | |a COMPUTERS |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast |0 (OCoLC)fst01036260 | |
| 650 | 7 | |a Computer Science. |2 hilcc | |
| 650 | 7 | |a Engineering & Applied Sciences. |2 hilcc | |
| 700 | 1 | |a Kühn, R. |q (Reimer), |d 1955- | |
| 700 | 1 | |a Sollich, P. |q (Peter) | |
| 776 | 0 | 8 | |i Print version: |a Coolen, A.C.C. (Anthony C.C.), 1960- |t Theory of neural information processing systems. |d Oxford : Oxford University Press, 2005 |z 0198530234 |z 0198530242 |w (DLC) 2006295078 |w (OCoLC)61301413 |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=149338 |z Texto completo |
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