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.
|Number of pages||7|
|Journal||Wiley Interdisciplinary Reviews: Computational Statistics|
|Publication status||Published - Nov 2009|
ASJC Scopus subject areas
- Statistics and Probability