Accession Number : ADA307230
Title : Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks.
Descriptive Note : Memorandum rept.,
Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB
Personal Author(s) : Jaakkola, Tommi S. ; Saul, Lawrence K. ; Jordan, Michael I.
PDF Url : ADA307230
Report Date : JAN 1996
Pagination or Media Count : 6
Abstract : Sigmoid type belief networks, a class of probabilistic neural networks, provide a natural framework for compactly representing probabilistic information in a variety of unsupervised and supervised learning problems. Often the parameters used in these networks need to be learned from examples. Unfortunately, estimating the parameters via exact probabilistic calculations (i.e, the EM-algorithm) is intractable even for networks with fairly small numbers of hidden units. We propose to avoid the infeasibility of the E step by bounding likelihoods instead of computing them exactly. We introduce extended and complementary representations for these networks and show that the estimation of the network parameters can be made fast (reduced to quadratic optimization) by performing the estimation in either of the alternative domains. The complementary networks can be used for continuous density estimation as well.
Descriptors : *NEURAL NETS, *ARTIFICIAL INTELLIGENCE, *LEARNING, DENSITY, OPTIMIZATION, COMPUTATIONS, NETWORKS, PARAMETERS, PROBABILITY, ESTIMATES, REDUCTION, QUADRATIC EQUATIONS, NUMBERS.
Subject Categories : Cybernetics
Human Factors Engineering & Man Machine System
Distribution Statement : APPROVED FOR PUBLIC RELEASE