By Topic

Class conditional density estimation using mixtures with constrained component sharing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
M. K. Titsias ; Dept. of Comput. Sci., Ioannina Univ., Greece ; A. Likas

We propose a generative mixture model classifier that allows for the class conditional densities to be represented by mixtures having certain subsets of their components shared or common among classes. We argue that, when the total number of mixture components is kept fixed, the most efficient classification model is obtained by appropriately determining the sharing of components among class conditional densities. In order to discover such an efficient model, a training method is derived based on the EM algorithm that automatically adjusts component sharing. We provide experimental results with good classification performance.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:25 ,  Issue: 7 )