Multi-class second-order cone programming support vector machines

被引：22

作者：

Lopez, Julio ^{[1
]}

Maldonado, Sebastian ^{[2
]}

机构：

[1] Univ Diego Portales, Fac Ingn, Santiago, Chile

[2] Univ Los Andes, Fac Ingn & Ciencias Aplicadas, Santiago, Chile

来源：

INFORMATION SCIENCES | 2016年 / 330卷

关键词：

Multi-class classification; Support vector machines; Second-order cone programming; Quadratic programming; Convex optimization; CLASSIFICATION; OPTIMIZATION; FORMULATIONS; SELECTION;

D O I：

10.1016/j.ins.2015.10.016

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents novel second-order cone programming (SOCP) formulations that determine a linear multi-class predictor using support vector machines (SVMs). We first extend the ideas of OvO (One-versus-One) and OvA (One-versus-All) SVM formulations to SOCP-SVM, providing two interesting alternatives to the standard SVM formulations. Additionally, we propose a novel approach (MC-SOCP) that simultaneously constructs all required hyperplanes for multi-class classification, based on the multi-class SVM formulation (MC-SVM). The use of conic constraints for each pair of training patterns in a single optimization problem provides an adequate framework for a balanced and effective prediction. (C) 2015 Elsevier Inc. All rights reserved.

引用

页码：328 / 341

页数：14

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