Skip to content

Graphical Models Foundations of Neural Computation

Best in textbook rentals since 2012!

ISBN-10: 0262600420

ISBN-13: 9780262600422

Edition: 2001

Authors: Michael I. Jordan, Terrence J. Sejnowski, Tomaso A. Poggio

List price: $49.00
Blue ribbon 30 day, 100% satisfaction guarantee!
what's this?
Rush Rewards U
Members Receive:
Carrot Coin icon
XP icon
You have reached 400 XP and carrot coins. That is the daily max!

Description:

This title seeks to exemplify the interplay between the general formal framework of graphical models and the exploration of new algorithms and architectures. The selections range from foundational papers to results at the cutting edge of research.
Customers also bought

Book details

List price: $49.00
Copyright year: 2001
Publisher: MIT Press
Publication date: 10/12/2001
Binding: Paperback
Pages: 434
Size: 6.00" wide x 9.00" long x 1.00" tall
Weight: 1.298
Language: English

Michael I. Jordan is Professor of Computer Science and of Statistics at the University of California, Berkeley, and recipient of the ACM/AAAI Allen Newell Award.

Terrence J. Sejnowski is Francis Crick Professor, Director of the Computational Neurobiology Laboratory, and a Howard Hughes Medical Institute Investigator at the Salk Institute for Biological Studies and Professor of Biology at the University of California, San Diego.

Series Foreword
Sources
Introduction
Probabilistic Independence Networks for Hidden Markov Probability Models
Learning and Relearning in Boltzmann Machines
Learning in Boltzmann Trees
Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space
Attractor Dynamics in Feedforward Neural Networks
Efficient Learning in Boltzmann Machines Using Linear Response Theory
Asymmetric Parallel Boltzmann Machines Are Belief Networks
Variational Learning in Nonlinear Gaussian Belief Networks
Mixtures of Probabilistic Principal Component Analyzers
Independent Factor Analysis
Hierarchical Mixtures of Experts and the EM Algorithm
Hidden Neural Networks
Variational Learning for Switching State-Space Models
Nonlinear Time-Series Prediction with Missing and Noisy Data
Correctness of Local Probability Propagation in Graphical Models with Loops
Index