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Large Margin Learning of Bayesian Classifiers Based on Gaussian Mixture Models

  • Franz Pernkopf
  • Michael Wohlmayr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6323)

Abstract

We present a discriminative learning framework for Gaussian mixture models (GMMs) used for classification based on the extended Baum-Welch (EBW) algorithm [1]. We suggest two criteria for discriminative optimization, namely the class conditional likelihood (CL) and the maximization of the margin (MM). In the experiments, we present results for synthetic data, broad phonetic classification, and a remote sensing application. The experiments show that CL-optimized GMMs (CL-GMMs) achieve a lower performance compared to MM-optimized GMMs (MM-GMMs), whereas both discriminative GMMs (DGMMs) perform significantly better than generatively learned GMMs. We also show that the generative discriminatively parameterized GMM classifiers still allow to marginalize over missing features, a case where generative classifiers have an advantage over purely discriminative classifiers such as support vector machines or neural networks.

Keywords

Support Vector Machine Bayesian Network Speech Recognition Gaussian Mixture Model Decision Boundary 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Franz Pernkopf
    • 1
  • Michael Wohlmayr
    • 1
  1. 1.Graz University of TechnologyGrazAustria

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