Support Vector Machine Based on Neighbor Edge Detection

doi:10.3969/j.issn.1006-2475.2015.03.004

Abstract

Abstract: This paper presents a Support Vector Machine (SVM) method based on neighbor edge detection, called Support Vector Machine based on Neighbor Edge Detection (ED_SVM), in order to solve the problem that there is low training efficiency and it can not solve the large scale data mining problems of normal SVM， because it needs save, compute and solve the large kernel matrix. By dividing data and obtaining the mixed clusters, this method extracts the important samples near the approximate optimal hyperplane by introducing neighbor edge detection technology into the SVM training process, which have the most important support vector information. The new training samples set is constructed by these new important samples to keep the distribution feature of original support vectors and compress the size of training dataset. Then the normal SVM is trained on these new training samples and the good generalization performance can be obtained with high learning efficiency synchronously. The experiment results demonstrate that the proposed ED_SVM model can obtain the high learning efficiency and testing accuracy simultaneously.

CLC Number:

TP18

WANG Xiu-hua, WU Li-fen. Support Vector Machine Based on Neighbor Edge Detection[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.03.004.

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