In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories,[1][2] SVMs are one of the most studied models, being based on statistical learning frameworks of VC theory proposed by Vapnik (1982, 1995) and Cher
TinySVM is an implementation of Support Vector Machines (SVMs) [Vapnik 95], [Vapnik 98] for the problem of pattern recognition. Support Vector Machines is a new generation learning algorithms based on recent advances in statistical learning theory, and applied to large number of real-world applications, such as text categorization, hand-written character recognition. List of Contents What's new Fe
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