Support Vector Machine

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. 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 into one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible.
Posts about Support Vector Machine
  • Vladimir Vapnik Joins Facebook AI Research

    … the capacity of a learning machine. Vladimir is rejoining some of his long-time collaborators: Jason Weston, Ronan Collobert and Yann LeCun. He is working on a new book and will be collaborating with FAIR (Fundamentals of Artificial Intelligence Research) research scientists to develop some of his new ideas on conditional density estimation, learning with privileged information and other topics. According to VentureBeat, Vapnik, Weston, Collobert and LeCun worked together at AT&T. …

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