ENGLISH

Graphical Models for Machine Learning and Digital Communication

By Frey B.J.

Book information

Publisher
MIT
Year
1998
ISBN
026206202X, 9780262062022
LCC
Q325.5 .F74 1998
Open Library ID
OL353730M
Language
english
Format
CHM
Filesize
1 MB (1470397 bytes)
Series
Adaptive Computation and Machine Learning
Pages
\0
Library
Kolxo3
Time added
2011-07-22 07:35:22

Description

A variety of problems in machine learning and digital communication deal with complex but structured natural or artificial systems. In this book, Brendan Frey uses graphical models as an overarching framework to describe and solve problems of pattern classification, unsupervised learning, data compression, and channel coding. Using probabilistic structures such as Bayesian belief networks and Markov random fields, he is able to describe the relationships between random variables in these systems and to apply graph-based inference techniques to develop new algorithms. Among the algorithms described are the wake-sleep algorithm for unsupervised learning, the iterative turbodecoding algorithm (currently the best error-correcting decoding algorithm), the bits-back coding method, the Markov chain Monte Carlo technique, and variational inference.

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