Now showing items 1-4 of 4

    • Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks 

      Jaakkola, Tommi S.; Saul, Lawrence K.; Jordan, Michael I. (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. ...
    • Learning from Incomplete Data 

      Ghahramani, Zoubin; Jordan, Michael I. (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 ...
    • A Note on the Generalization Performance of Kernel Classifiers with Margin 

      Evgeniou, Theodoros; Pontil, Massimiliano (2000-05-01)
      We present distribution independent bounds on the generalization misclassification performance of a family of kernel classifiers with margin. Support Vector Machine classifiers (SVM) stem out of this class of machines. The ...
    • On Convergence Properties of the EM Algorithm for Gaussian Mixtures 

      Jordan, Michael; Xu, Lei (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 ...