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!!!