Support vector machines

被引：133
|
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.
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页码：917 / 937
页数：21
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