Large Margin Learning of Bayesian Classifiers Based on Gaussian Mixture Models
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.
Cite
Text
Pernkopf and Wohlmayr. "Large Margin Learning of Bayesian Classifiers Based on Gaussian Mixture Models." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15939-8_4Markdown
[Pernkopf and Wohlmayr. "Large Margin Learning of Bayesian Classifiers Based on Gaussian Mixture Models." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/pernkopf2010ecmlpkdd-large/) doi:10.1007/978-3-642-15939-8_4BibTeX
@inproceedings{pernkopf2010ecmlpkdd-large,
title = {{Large Margin Learning of Bayesian Classifiers Based on Gaussian Mixture Models}},
author = {Pernkopf, Franz and Wohlmayr, Michael},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2010},
pages = {50-66},
doi = {10.1007/978-3-642-15939-8_4},
url = {https://mlanthology.org/ecmlpkdd/2010/pernkopf2010ecmlpkdd-large/}
}