Support Vector Machines

Author:

Guenther Nick1,Schonlau Matthias1

Affiliation:

1. University of Waterloo Waterloo, Canada

Abstract

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.

Publisher

SAGE Publications

Subject

Mathematics (miscellaneous)

Reference22 articles.

1. A User’s Guide to Support Vector Machines

2. LIBSVM

3. A tutorial onν-support vector machines

4. Support-vector networks

5. HastieT. 2003. Support vector machines, kernel logistic regression, and boosting. http://web.stanford.edu/∼hastie/Papers/svmtalk.pdf.

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