"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;

September 05, 2016

Day #31 - Support Vector Machines

SVM
  • Support Vector Machines
  • Widest Street approach separating +ve and -ve classes, Separations as wide as possible
  • SVM works on classifying only two classes
  • Hard SVM (Strictly linearly separable)
  • Soft SVM (Minimize how they fall on another side, Constant C to minimize how much allow one point go on another side)
  • Kernel Functions perform transformation of data
  • Using Kernel function we simulate idea of finding linear separator 
  • Kernels take data into higher dimensional space
  • Other Key concepts discussed (Lagrange Multipliers, Quadratic Optimization problem)
  • Lagrangian constraint transform from 1D to 2D data
  • SVM (Linear way of approximation)
  • Types of Kernels - Polynomial Kernel, Radial Basis Function Kernel, Sigmoid Kernel
Maths Behind it - Link
Good Relevant Read - SVM

Happy Data Analysis!!!
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