Support vector machines

被引：140

作者：

Guenther, Nick ^{[1
,2
]}

Schonlau, Matthias ^{[3
]}

机构：

[1] Univ Waterloo, Dept Stat, Waterloo, ON, Canada

[2] Univ Waterloo, Sch Comp Sci, Waterloo, ON, Canada

[3] Univ Waterloo, Dept Stat & Actuarial Sci, Waterloo, ON, Canada

来源：

STATA JOURNAL | 2016年 / 16卷 / 04期

关键词：

st0461; svmachines; svm; statistical learning; machine learning; support vector machines; TUTORIAL;

D O I：

10.1177/1536867X1601600407

中图分类号：

O1 [数学]; C [社会科学总论];

学科分类号：

03 ; 0303 ; 0701 ; 070101 ;

摘要：

Support vector machines are statistical-and machine-learning techniques with the primary goal of prediction. They can be applied to continuous, binary, and categorical outcomes analogous to Gaussian, logistic, and multinomial regression. We introduce a new command for this purpose, svmachines. This package is a thin wrapper for the widely deployed libsvm (Chang and Lin, 2011, ACM Transactions on Intelligent Systems and Technology 2(3): Article 27). We illustrate svmachines with two examples.

引用

页码：917 / 937

页数：21

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