Efficient Optimization of Multi-class Support Vector Machines with MSVMpack

被引：2

作者：

Didiot, Emmanuel ^{[1
]}

Lauer, Fabien ^{[1
]}

机构：

[1] Univ Lorraine, CNRS, Inria, LORIA, Nancy, France

来源：

MODELLING, COMPUTATION AND OPTIMIZATION IN INFORMATION SYSTEMS AND MANAGEMENT SCIENCES - MCO 2015 - PT II | 2015年 / 360卷

关键词：

Quadratic programming; classification; support vector machines; parallel computing; SVM;

D O I：

10.1007/978-3-319-18167-7_3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the field of machine learning, multi-class support vector machines (M-SVMs) are state-of-the-art classifiers with training algorithms that amount to convex quadratic programs. However, solving these quadratic programs in practice is a complex task that typically cannot be assigned to a general purpose solver. The paper describes the main features of an efficient solver for M-SVMs, as implemented in the MSVMpack software. The latest additions to this software are also highlighted and a few numerical experiments are presented to assess its efficiency.

引用

页码：23 / 34

页数：12

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