Basic Concept

  • supervised machine learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis
  • non-probabilistic binary linear classifier
  • 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
    - new examples are mapped into the same space
  • Objective : choose the line (hyperplane) which has the largest margin
  • Kernel tricks can be used for nonlinear data

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