TY - GEN
T1 - Classifier fusion using shared sampling distribution for boosting
AU - Barbu, Costin
AU - Iqbal, Raja
AU - Jing, Peng
PY - 2005/12/1
Y1 - 2005/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33845540017&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2005.40
DO - 10.1109/ICDM.2005.40
M3 - Conference contribution
AN - SCOPUS:33845540017
SN - 0769522785
SN - 9780769522784
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 34
EP - 41
BT - Proceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
T2 - 5th IEEE International Conference on Data Mining, ICDM 2005
Y2 - 27 November 2005 through 30 November 2005
ER -