title: A user's guide to support vector machines.
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.
authors.affiliation: Department of Computer Science, Colorado State University, Fort Collins, CO, USA.
authors.forename: Asa
authors.initials: A
authors.lastname: Ben-Hur
authors.forename: Jason
authors.initials: J
authors.lastname: Weston
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D057225:Data Mining; D016208:Databases, Factual; D016014:Linear Models; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D012984:Software