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ICML
2003
IEEE

Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers

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Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers
We show that a classifier based on Gaussian mixture models (GMM) can be trained discriminatively to improve accuracy. We describe a training procedure based on the extended Baum-Welch algorithm used in speech recognition. We also compare the accuracy and degree of sparsity of the new discriminative GMM classifier with those of generative GMM classifiers, and of kernel classifiers, such as support vector machines (SVM) and relevance vector machines (RVM).
Aldebaro Klautau, Nikola Jevtic, Alon Orlitsky
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2003
Where ICML
Authors Aldebaro Klautau, Nikola Jevtic, Alon Orlitsky
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