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OverviewTheory 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. Full Product DetailsAuthor: A.C.C. Coolen , R. Kuehn , P. SollichPublisher: Oxford University Press Imprint: Oxford University Press Dimensions: Width: 17.90cm , Height: 3.80cm , Length: 25.40cm Weight: 1.173kg ISBN: 9780198530237ISBN 10: 0198530234 Pages: 586 Publication Date: 28 July 2005 Audience: College/higher education , Tertiary & Higher Education Format: Hardback Publisher's Status: Active Availability: To order ![]() Stock availability from the supplier is unknown. We will order it for you and ship this item to you once it is received by us. Table of ContentsI 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 ReferencesReviewsThe 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 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 Author InformationTab Content 6Author Website:Countries AvailableAll regions |