Now showing items 1-2 of 2
Learning from Incomplete Data
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
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. ...