Support vector Machine
Support vector machine Set of methods for supervised statistical learning In machine learning , support vector machines ( SVMs , also support vector networks ) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis . Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik , 1995, Vapnik et al., 1997 [ citation needed ] ) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974). Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non- probabilistic ...