A User’s Guide to Support Vector Machines
Methods in molecular biologyPublished 30 October 2009
Asa Ben‐Hur, Jason Weston
Citations819
SJR quartileQ4
SJR score0.35
SNIP0.87
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TL;DR
This work provides a basic understanding of the theory behind SVMs and focuses on their use in practice, describing the effect of the SVM parameters on the resulting classifier, how to select good values for those parameters, data normalization, factors that affect training time, and software for training SVMs.
Abstract
The Support Vector Machine (SVM) is a widely used classifier in bioinformatics. Obtaining the best results with SVMs requires an understanding of their workings and the various ways a user can influence their accuracy. We provide the user with a basic understanding of the theory behind SVMs and focus on their use in practice. We describe the effect of the SVM parameters on the resulting classifier, how to select good values for those parameters, data normalization, factors that affect training time, and software for training SVMs.
Keywords
Computer ScienceBiochemistry, Genetics and Molecular Biology
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