计算机与现代化Issue(3)：15-19,25,6.DOI:10.3969/j.issn.1006-2475.2015.03.004
基于近邻边缘检测的支持向量机
Support Vector Machine Based on Neighbor Edge Detection
王秀华 1武丽芬1
作者信息
- 1. 晋中学院信息技术与工程学院，山西 晋中 030619
- 折叠
摘要
Abstract
This paper presents a Support Vector Machine ( SVM) method based on neighbor edge detection, called Support Vec-tor 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 opti-mal 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 re-sults demonstrate that the proposed ED_SVM model can obtain the high learning efficiency and testing accuracy simultaneously.关键词
支持向量机/边缘检测/支持向量/泛化性能/学习效率/ED_SVM算法Key words
support vector machine/edge detection/support vector/generalization performance/learning efficiency/ED_SVM algorithm分类
信息技术与安全科学引用本文复制引用
王秀华,武丽芬..基于近邻边缘检测的支持向量机[J].计算机与现代化,2015,(3):15-19,25,6.
