Support Vector Machines

Support Vector Machines are the set of related supervised machine learning algorithm capable of delivering higher performance in terms of classification and regression accuracy. In this algorithm each data item is plotted as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate. SVM utilises an optimum linear separating hyper plane to separate two data sets in a feature space. This optimum hyper plane is produced by maximizing minimum margin between the two sets. Therefore the resulting hyper plane will only be depended on border training patterns called support vectors.

  • Support Vector Machines for Transduction
  • Support Vector Machines for Predictive Modeling in Heterogeneous Catalysis
  • Fuzzy Support Vector Machines
  • Kernel Methods and Support Vector Machines

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