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.
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