Support vector machines

Alessia Mammone, Marco Turchi, Nello Cristianini

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)283-289
Number of pages7
JournalWiley Interdisciplinary Reviews: Computational Statistics
Issue number3
Publication statusPublished - Nov 2009

ASJC Scopus subject areas

  • Statistics and Probability


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