Massive data classification via unconstrained support vector machines

被引：8

作者：

Mangasarian, O. L. ^{[1
]}

Thompson, M. E. ^{[1
]}

机构：

[1] Univ Wisconsin, Dept Comp Sci, Madison, WI 53706 USA

来源：

JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS | 2006年 / 131卷 / 03期

基金：

美国国家科学基金会;

关键词：

data classification; support vector machines; linear programming; unconstrained minimization; Newton method;

D O I：

10.1007/s10957-006-9157-x

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

A highly accurate algorithm, based on support vector machines formulated as linear programs (Refs. 1-2), is proposed here as a completely unconstrained minimization problem (Ref. 3). Combined with a chunking procedure (Ref. 4), this approach, which requires nothing more complex than a linear equation solver, leads to a simple and accurate method for classifying million-point datasets. Because a 1-norm support vector machine underlies the proposed approach, the method suppresses input space features as well. A state-of-the-art linear programming package (CPLEX, Ref. 5) fails to solve problems handled by the proposed algorithm.

引用

页码：315 / 325

页数：11

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