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,...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Coolen, A. C. C. (Anthony C. C.), 1960-
Otros Autores: Kühn, R. (Reimer), 1955-, Sollich, P. (Peter)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Oxford : Oxford University Press, 2005.
Temas:
Neural networks (Computer science)
Réseaux neuronaux (Informatique)
COMPUTERS > Neural Networks.
Computer Science.
Engineering & Applied Sciences.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Machine generated contents note: pt. I Introduction to neural networks
  • 1. General introduction
  • 2. Layered networks
  • 3. Recurrent networks with binary neurons
  • 4. Notes and suggestions for further reading
  • pt. II Advanced neural networks
  • 5. Competitive unsupervised learning processes
  • 6. Bayesian techniques in supervised learning
  • 7. Gaussian processes
  • 8. Support vector machines for binary classification
  • 9. Notes and suggestions for further reading
  • pt. III Information theory and neural networks
  • 10. Measuring information
  • 11. Identification of entropy as an information measure
  • 12. Building blocks of Shannon's information theory
  • 13. Information theory and statistical inference
  • 14. Applications to neural networks
  • 15. Notes and suggestions for further reading
  • pt. IV Macroscopic analysis of dynamics
  • 16. Network operation : macroscopic dynamics
  • 17. Dynamics of online learning in binary perceptions
  • 18. Dynamics of online gradient descent learning
  • 19. Notes and suggestions for further reading
  • pt. V Equilibrium statistical mechanics of neural networks
  • 20. Basics of equilibrium statistical mechanics
  • 21. Network operation : equilibrium analysis
  • 22. Gardner theory of task realizability
  • 23. Notes and suggestions for further reading
  • App. A Probability theory in a nutshell
  • App. B Conditions for the central limit theorem to apply
  • App. C Some simple summation identities
  • App. D Gaussian integrals and probability distributions
  • App. E Matrix identities
  • App. F [delta]-distribution
  • App. G Inequalities based on convexity
  • App. H Metrics for parametrized probability distributions.

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