DocumentCode :
1265709
Title :
Clamping in Boltzmann machines
Author :
Livesey, Mike
Author_Institution :
Dept. of Comput. Sci., St. Andrews Univ., UK
Volume :
2
Issue :
1
fYear :
1991
fDate :
1/1/1991 12:00:00 AM
Firstpage :
143
Lastpage :
148
Abstract :
A certain assumption that appears in the proof of correctness of the standard Boltzmann machine learning procedure is investigated. The assumption, called the clamping assumption, concerns the behavior of a Boltzmann machine when some of its units are clamped to a fixed state. It is argued that the clamping assumption is essentially an assertion of the time reversibility of a certain Markov chain underlying the behavior of the Boltzmann machine. As such, the clamping assumption is generally false, though it is certainly true of the Boltzmann machines themselves. The author also considers how the concept of the Boltzmann machine may be generalized while retaining the validity of the clamping assumption
Keywords :
Markov processes; learning systems; neural nets; Boltzmann machines; Markov chain; clamping assumption; learning; time reversibility; Clamps; Computer science; Machine learning; Probability distribution; Simulated annealing; State-space methods; Stochastic processes; Stochastic systems;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/72.80301
Filename :
80301
Link To Document :
بازگشت