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

    … Facebook’s artificial-intelligence unit, Facebook AI Research, announced the addition of Vladimir Vapnik, from the University of London. The hire was announced in a post on the Facebook AI Research page, in which Vapnik was pictured with Yann LeCun, who was tapped to lead the social network’s AI lab last December: We are delighted to announce…

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