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 :
بازگشت