Research on large scale SVM classification based on boundary K-nearest

doi:10.3778/j.issn.1002-8331.2009.23.005

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (23): 15-17.DOI: 10.3778/j.issn.1002-8331.2009.23.005

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Research on large scale SVM classification based on boundary K-nearest

FENG Guo-he

College of Economics and Management，South China Normal University，Guangzhou 510006，China

Received:2009-05-06 Revised:2009-06-07 Online:2009-08-11 Published:2009-08-11
Contact: FENG Guo-he

边界K邻近大样本支持向量机分类

奉国和

华南师范大学经济管理学院信息管理系，广州 510006

通讯作者: 奉国和

Abstract

Abstract: The problem of occupying much memory and slow training speed will come forth for Support Vector Machine（SVM） with large scale training set.This paper puts forward a boundary K-NN SVM algorithm，searching for possible support vectors with K-NN and training SVM classifier based on such support vectors.Experiment shows that modified algorithm training speed is advanced.

Key words: upport Vector Machine（SVM）, large-scale samples, classification

摘要： 针对大样本支持向量机内存开销大、训练速度慢的缺点，提出了一种改进的支持向量机算法。算法先利用KNN方法找出可能支持向量，然后利用SVM在可能支持向量集上训练得到分类器。实验表明改进算法训练速度提高明显。

关键词: 支持向量机, 大样本, 分类

CLC Number:

TP316

FENG Guo-he. Research on large scale SVM classification based on boundary K-nearest[J]. Computer Engineering and Applications, 2009, 45(23): 15-17.

奉国和. 边界K邻近大样本支持向量机分类[J]. 计算机工程与应用, 2009, 45(23): 15-17.

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Research on large scale SVM classification based on boundary K-nearest

边界K邻近大样本支持向量机分类

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