Fast Support Vector Training by Newton's Method

被引：0

作者：

Abe, Shigeo ^{[1
]}

机构：

[1] Kobe Univ, Nada Ku, Kobe, Hyogo 657, Japan

来源：

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2011, PT II | 2011年 / 6792卷

关键词：

SMO;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We discuss a fast training method of support vector machines using Newton's method combined with fixed-size chunking. To speed up training, we limit the number of upper or lower bounded variables in the working set to two so that the corrections of the variables do not violate the bounding conditions. If similar working sets occur alternately, we merge these two working sets into one, and if similar working sets occur consecutively, we use incremental Cholesky factorization in calculating corrections. By computer experiments, we show that the proposed method is comparable to or faster than SMO (Sequential minimum optimization) using the second order information.

引用

页码：143 / 150

页数：8

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