Project Type:
Master/Diploma Thesis
Student:
Wolfgang Roth

Gaussian Mixture Models (GMMs) are a popular choice for modeling probability density functions, with a vast number of applications in speech and audio technologies and machine learning in general. For classification, one can use classdependent GMMs and easily build a probabilistic classifier using Bayes rule. The problem here is, that the classical way to train GMMs, i.e. using the maximum likelihood principle and applying the expectationmaximization algorithm, is not aware of classification. This generative training typically yields suboptimal classifiers. On the other hand, when the classifier is trained in a purely discriminative way, the model actually looses most of its probabilistic semantics.
In this master/diploma thesis, the task is to apply a recently proposed hybrid generativediscriminative learning criterion for GMM training. The goal of this approach is to
You should have
[1] C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
[2] R. Peharz, S. Tschiatschek, F. Pernkopf, "The Most Generative Maximum Margin Bayesian Networks", ICML, 2013.
[3] F. Pernkopf "Large Margin Learning of Bayesian Classifiers based on Gaussian Mixture Models", ECML, 2010