Free Graphical Models: Foundations Of Neural Computation (Computational Neuroscience) Ebooks Online
Graphical models use graphs to represent and manipulate joint probability distributions. They have their roots in artificial intelligence, statistics, and neural networks. The clean mathematical formalism of the graphical models framework makes it possible to understand a wide variety of network-based approaches to computation, and in particular to understand many neural network algorithms and architectures as instances of a broader probabilistic methodology. It also makes it possible to identify novel features of neural network algorithms and architectures and to extend them to more general graphical models.This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithms and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research.Contributors H. Attias, C. M. Bishop, B. J. Frey, Z. Ghahramani, D. Heckerman, G. E. Hinton, R. Hofmann, R. A. Jacobs, Michael I. Jordan, H. J. Kappen, A. Krogh, R. Neal, S. K. Riis, F. B. Rodríguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss.
Series: Computational Neuroscience Series
Paperback: 435 pages
Publisher: A Bradford Book; 1st edition (September 1, 2001)
Language: English
ISBN-10: 0262600420
ISBN-13: 978-0262600422
Product Dimensions: 6 x 1 x 9 inches
Shipping Weight: 1.4 pounds
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Best Sellers Rank: #1,506,891 in Books (See Top 100 in Books) #160 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Neural Networks #210 in Books > Science & Math > Mathematics > Applied > Graph Theory #265 in Books > Science & Math > Mathematics > Pure Mathematics > Combinatorics
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