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Sequential em for unsupervised adaptive gaussian mixture model based classifier

Awwad Shiekh Hasan, B and Gan, JQ (2009) Sequential em for unsupervised adaptive gaussian mixture model based classifier. In: UNSPECIFIED, ? - ?.

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Abstract

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 > Computer Science and Electronic Engineering, School of
Depositing User: Users 161 not found.
Date Deposited: 13 Dec 2012 16:48
Last Modified: 17 Aug 2017 18:07
URI: http://repository.essex.ac.uk/id/eprint/4147

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