Graphical models foundations of neural computation

• Other Authors: Jordan, Michael Irwin, 1956-, Sejnowski, Terrence J.
• Format: eBook
• Language: English
• Published: Cambridge, Mass. : MIT Press, c2001.
• Series: Computational neuroscience.
• Subjects: Neural networks (Computer science); Computer graphics.; elektronické knihy; electronic books
• ISBN: 9780262291200
• Physical Description: 1 online zdroj (xxiv, 421 p.) : ill.

Table of Contents:

• 1 Probabilistic Independence Networks for Hidden Markov Probability Models / Padhraic Smyth, David Heckerman, Michael I. Jordan 1
• 2 Learning and Relearning in Boltzmann Machines / G.E. Hinton, T.J. Sejnowski 45
• 3 Learning in Boltzmann Trees / Lawrence Saul, Michael I. Jordan 77
• 4 Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space / Geoffrey E. Hinton 89
• 5 Attractor Dynamics in Feedforward Neural Networks / Lawrence K. Saul, Michael I. Jordan 97
• 6 Efficient Learning in Boltzmann Machines Using Linear Response Theory / H.J. Kappen, F.B. Rodriguez 121
• 7 Asymmetric Parallel Boltzmann Machines Are Belief Networks / Radford M. Neal 141
• 8 Variational Learning in Nonlinear Gaussian Belief Networks / Brendan J. Frey, Geoffrey E. Hinton 145
• 9 Mixtures of Probabilistic Principal Component Analyzers / Michael E. Tipping, Christopher M. Bishop 167
• 10 Independent Factor Analysis / H. Attias 207
• 11 Hierarchical Mixtures of Experts and the EM Algorithm / Michael I. Jordan, Robert A. Jacobs 257
• 12 Hidden Neural Networks / Anders Krogh, Soren Kamaric Riis 291
• 13 Variational Learning for Switching State-Space Models / Zoubin Ghahramani, Geoffrey E. Hinton 315
• 14 Nonlinear Time-Series Prediction with Missing and Noisy Data / Volker Tresp, Reimar Hofmann 349
• 15 Correctness of Local Probability Propagation in Graphical Models with Loops / Yair Weiss 367.