Support vector machines
Wiley Interdisciplinary Reviews Computational Statistics2009Vol. 1(3), pp. 283–289
Citations Over TimeTop 10% of 2009 papers
Abstract
Abstract Support vector machines (SVMs) are a family of machine learning methods, originally introduced for the problem of classification and later generalized to various other situations. They are based on principles of statistical learning theory and convex optimization, and are currently used in various domains of application, including bioinformatics, text categorization, and computer vision. Copyright © 2009 John Wiley & Sons, Inc. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification
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