DocumentCode
2754183
Title
Bagging in computer vision
Author
Draper, Bruce A. ; Baek, Kyungim
Author_Institution
Dept. of Comput. Sci., Colorado State Univ., Fort Collins, CO, USA
fYear
1998
fDate
23-25 Jun 1998
Firstpage
144
Lastpage
149
Abstract
Previous research has shown that aggregated predictors improve the performance of non-parametric function approximation techniques. This paper presents the results of applying aggregated predictors to a computer vision problem, and shows that the method of bagging significantly improves performance. In fact, the results are better than those previously reported on other domains. This paper explains this performance in terms of the variance and bias
Keywords
computer vision; function approximation; neural nets; object recognition; prediction theory; aggregated predictors; bagging; bias; computer vision; nonparametric function approximation techniques; variance; Application software; Bagging; Computer science; Computer vision; Function approximation; Navigation; Neural networks; Object recognition; Pattern recognition; Target recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on
Conference_Location
Santa Barbara, CA
ISSN
1063-6919
Print_ISBN
0-8186-8497-6
Type
conf
DOI
10.1109/CVPR.1998.698601
Filename
698601
Link To Document