By Topic

Classifier fusion using shared sampling distribution for boosting

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

3 Author(s)
C. Barbu ; Dept. of Electr. Eng. & Comput. Sci., Tulane Univ., New Orleans, LA, USA ; R. Iqbal ; Jing Peng

We present a new framework for classifier fusion that uses a shared sampling distribution for obtaining a weighted classifier ensemble. The weight update process is self regularizing as subsequent classifiers trained on the disjoint views rectify the bias introduced by any classifier in preceding iterations. We provide theoretical guarantees that our approach indeed provides results which are better than the case when boosting is performed separately on different views. The results are shown to outperform other classifier fusion strategies on a well known texture image database.

Published in:

Fifth IEEE International Conference on Data Mining (ICDM'05)

Date of Conference:

27-30 Nov. 2005