计算机工程与应用 ›› 2009, Vol. 45 ›› Issue (23): 15-17.DOI: 10.3778/j.issn.1002-8331.2009.23.005
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奉国和
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FENG Guo-he
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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
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
中图分类号:
TP316
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.
FENG Guo-he. Research on large scale SVM classification based on boundary K-nearest[J]. Computer Engineering and Applications, 2009, 45(23): 15-17.
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链接本文: http://cea.ceaj.org/CN/10.3778/j.issn.1002-8331.2009.23.005
http://cea.ceaj.org/CN/Y2009/V45/I23/15
