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http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7189| Title: | Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks |
| Keywords: | AI MIT Artificial Intelligence Belief networks Probabilistic networks EM algorithm Density estimation Likelihood bounds |
| Issue Date: | 9-Oct-2013 |
| Description: | 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. |
| URI: | http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
| Other Identifiers: | AIM-1560 CBCL-129 http://hdl.handle.net/1721.1/7189 |
| Appears in Collections: | MIT Items |
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