@inproceedings{b30e9933da894bb9b1463426464e6475,
title = "Maximum Gaussian mixture model for classification",
abstract = "There are a variety of models and algorithms that solves classification problems. Among these models, Maximum Gaussian Mixture Model (MGMM) is a model we proposed earlier that describes data using the maximum value of Gaussians. Expectation Maximization (EM) algorithm can be used to solve this model. In this paper, we propose a multiEM approach to solve MGMM and to train MGMM based classifiers. This approach combines multiple MGMMs solved by EM into a classifier. The classifiers trained with this approach on both artificial and real life datasets were tested to have good performance with 10-fold cross validation.",
keywords = "Classification, Maximum Gaussian mixture model",
author = "Jiehao Zhang and Xianbin Hong and Guan, {Sheng Uei} and Xuan Zhao and Xin Huang and Nian Xue",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 8th International Conference on Information Technology in Medicine and Education, ITME 2016 ; Conference date: 23-12-2016 Through 25-12-2016",
year = "2017",
month = jul,
day = "12",
doi = "10.1109/ITME.2016.0139",
language = "English",
series = "Proceedings - 2016 8th International Conference on Information Technology in Medicine and Education, ITME 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "587--591",
editor = "Ying Dai and Shaozi Li and Yun Cheng",
booktitle = "Proceedings - 2016 8th International Conference on Information Technology in Medicine and Education, ITME 2016",
}