DocumentCode
381405
Title
BPMs versus SVMs for image classification
Author
Wu, Gang ; Chang, Edward ; Li, Chung-Sheng
Author_Institution
Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA
Volume
2
fYear
2002
fDate
2002
Firstpage
505
Abstract
The Bayes point machine (BPM) has been demonstrated theoretically to have better learning ability than the support vector machine (SVM). We describe these two machines and tell how they differ. We empirically compare the performance of the BPM and the SVM on an image dataset. We conclude that the SVM is more attractive for the image classification task because it requires a much shorter training time, despite the fact that the BPM achieves slightly higher classification accuracy.
Keywords
Bayes methods; image classification; learning (artificial intelligence); learning automata; visual databases; Bayes point machine; SVM; image classification; image dataset; learning ability; support vector machine; Bayesian methods; Image classification; Image retrieval; Machine learning; Multilayer perceptrons; Polynomials; Quadratic programming; Statistical learning; Support vector machine classification; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Multimedia and Expo, 2002. ICME '02. Proceedings. 2002 IEEE International Conference on
Print_ISBN
0-7803-7304-9
Type
conf
DOI
10.1109/ICME.2002.1035658
Filename
1035658
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