DocumentCode
384279
Title
How many classifiers do I need?
Author
Schiele, Bernt
Author_Institution
Comput. Sci. Dept., Eidgenossische Tech. Hochschule, Zurich, Switzerland
Volume
2
fYear
2002
fDate
2002
Firstpage
176
Abstract
Combining multiple classifiers promises to increase performance and robustness of a classification task. Currently, the understanding which combination scheme should be used and the ability to quantify the expected benefit is inadequate. This paper attempts to quantify the performance and robustness gain for different combination schemes and for two classifier types. The results indicate that the combination of a small number of classifiers may already result in a substantial performance gain. Also, the increase in robustness can be substantial by combining an adequate number of classifiers.
Keywords
pattern classification; probability; binary classifiers; complementary classifiers; majority vote; multiple classifiers; pattern classification; performance; probability; product-rule; redundant classifiers; robustness; sum-rule; Computer science; Computer vision; Equations; Noise robustness; Pattern recognition; Performance gain; Probability; Voting;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2002. Proceedings. 16th International Conference on
ISSN
1051-4651
Print_ISBN
0-7695-1695-X
Type
conf
DOI
10.1109/ICPR.2002.1048266
Filename
1048266
Link To Document