Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks

Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks

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....

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Bibliographic Details
Main Authors: Jaakkola, Tommi S., Saul, Lawrence K., Jordan, Michael I.
Language:en_US
Published: 2004
Subjects:
AI
MIT
Artificial Intelligence
Belief networks
Probabilistic networks
EM algorithm
Density estimation
Likelihood bounds
Online Access:http://hdl.handle.net/1721.1/7189

Internet

http://hdl.handle.net/1721.1/7189

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