Support vector machines

被引：148
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
收藏
页码：917 / 937
页数：21
相关论文
共 22 条
[1]  
Ben-Hur A, 2010, METHODS MOL BIOL, V609, P223, DOI 10.1007/978-1-60327-241-4_13
[2]   LIBSVM: A Library for Support Vector Machines [J].
Chang, Chih-Chung ;
Lin, Chih-Jen .
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2011, 2 (03)
[3]   A tutorial on v-support vector machines [J].
Chen, PH ;
Lin, CJ ;
Schölkopf, B .
APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, 2005, 21 (02) :111-136
[4]  
CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411
[5]  
Hastie T, 2003, SUPPORT VECTOR MACHI
[6]  
Hastie T., 2009, The Elements of Statistical Learning: Data Mining, Inference and Prediction, V2, P1
[7]  
Hsu C.W., 2003, PRACTICAL GUIDE SUPP
[8]   A comparison of methods for multiclass support vector machines [J].
Hsu, CW ;
Lin, CJ .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2002, 13 (02) :415-425
[9]  
James G, 2013, SPRINGER TEXTS STAT, V103, P1, DOI 10.1007/978-1-4614-7138-7_1
[10]  
Joachims T, 1999, ADVANCES IN KERNEL METHODS, P169
123