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

    … that Vladimir Vapnik has joined Facebook AI Research. Vladimir is universally known in the machine-learning and statistics communities as the father of statistical learning theory and the co-inventor of the Support Vector Machine method. One of the key concepts of learning theory bears his name: the Vapnik-Chervonenkis Dimension, which measures…

    David Cohen/ AllFacebookin Social- 5 readers -
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