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Theory of neural information processing systems
A.C.C. Coolen, R. Kuehn, and P. Sollich
586 pages
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numerous figures
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246x171mm
978-0-19-853023-7
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Hardback
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28 July 2005
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This item is printed to order and supplied on a firm sale basis. Items which are printed to order are normally despatched and charged within 5-10 days.
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- Interdisciplinary comprehensive text aimed at physicists, mathematicians, and computer scientists
- Contains multiple exercises on each topic
- Mathematically rigorous
- Highlights latest research and future work in neural networks
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 dsicipline, 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.Readership: Graduates and researchers in physics, mathematics, computer science, and other quantitative disciplines.
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A.C.C. Coolen, Professor of Applied Mathematics, Department of Mathematics, King's College, London, R. Kuehn, Lecturer in Applied Mathematics, Department of Mathematics, King's College, London, and P. Sollich, Professor of Statistical Mechanics, Department of Mathematics, King's College, London
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"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 graduate courses in artificial neural networks. It is completely self-contained and includes also a 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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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
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