Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers
Abstract
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). ICML Proceedings of the Twentieth International Conference on Machine Learning
Cite
Text
Klautau et al. "Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers." International Conference on Machine Learning, 2003.Markdown
[Klautau et al. "Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers." International Conference on Machine Learning, 2003.](https://mlanthology.org/icml/2003/klautau2003icml-discriminative/)BibTeX
@inproceedings{klautau2003icml-discriminative,
title = {{Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers}},
author = {Klautau, Aldebaro and Jevtic, Nikola and Orlitsky, Alon},
booktitle = {International Conference on Machine Learning},
year = {2003},
pages = {353-360},
url = {https://mlanthology.org/icml/2003/klautau2003icml-discriminative/}
}