Pre-extracting method for SVM classification based on the non-parametric K-NN rule

With the increase of the training set’s size, the efficiency of support vector machine (SVM) classifier will be confined. To solve such a problem, a novel preextracting method for SVM classification is proposed in this paper. In SVM classification, only support vectors (SVs) have significant influence on the optimization result. We adopt a non-parametric k-NN rule called relative neighborhood graph (RNG) to extract the probable SVs from all the training samples. Experimental results verify that the approach proposed can effectively reduce training set’s size and accelerate the learning speed. At the same time, the classification accuracies are still competitive.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Deqiang Han, Chongzhao Han, Yi Yang, Yu Liu, Wentao Mao
• Keywords: Computer Vision, ICPR 2008, Support Vectors, SVM Classification, Training Set