Short Description

Recently, we developed a discriminative learning framework for Gaussian mixture models (GMMs) [1] . We suggest two criteria for discriminative optimization, namely the class conditional likelihood and the maximization of the margin.

The aim of this project is to extend this work to Hidden Markov Models and to apply this to typical sequence classification tasks.

Your Profile/Requirements

• The candidate should be interested in machine learning, applied mathematics/statistics, Matlab programming, and algorithms. Interested candidates are encouraged to ask for further information. Additionally, the supervision of own projects in one of the above mention fields is possible.

Contact:

Franz Pernkopf (pernkopf@tugraz.at or 0316/873 4436)

References

[1] F. Pernkopf, M. Wohlmayr, “Large Margin Learning of Bayesian Classifiers based on Gaussian Mixture Models”, European Conference on Machine Learning (ECML), pp. 50-66, 2010.