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
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;
Conference_Titel :
Multimedia and Expo, 2002. ICME '02. Proceedings. 2002 IEEE International Conference on
Print_ISBN :
0-7803-7304-9
DOI :
10.1109/ICME.2002.1035658
