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Learning from Incomplete Data
(1995-01-24)
Real-world learning tasks often involve high-dimensional data sets with complex patterns of missing features. In this paper we review the problem of learning from incomplete data from two statistical perspectives---the ...
Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks
(1996-02-09)
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. ...
On Convergence Properties of the EM Algorithm for Gaussian Mixtures
(1995-04-21)
"Expectation-Maximization'' (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a ...