Research Repository

Sequential EM for Unsupervised Adaptive Gaussian Mixture Model Based Classifier

Awwad Shiekh Hasan, Bashar and Gan, John Q (2009) Sequential EM for Unsupervised Adaptive Gaussian Mixture Model Based Classifier. In: UNSPECIFIED, ? - ?.

Full text not available from this repository.


In this paper we present a sequential expectation maximization algorithm to adapt in an unsupervised manner a Gaussian mixture model for a classification problem. The goal is to adapt the Gaussian mixture model to cope with the non-stationarity in the data to classify and hence preserve the classification accuracy. Experimental results on synthetic data show that this method is able to learn the time-varying statistical features in data by adapting a Gaussian mixture model online. In order to control the adaptation method and to ensure the stability of the adapted model, we introduce an index to detect when the adaptation would fail. © 2009 Springer Berlin Heidelberg.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Published proceedings: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 13 Dec 2012 16:48
Last Modified: 23 Sep 2022 18:29

Actions (login required)

View Item View Item