"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" ;

April 14, 2016

Basics - SUPPORT VECTOR MACHINES

Good Reading from link

Key Notes
  • Allow non-linear decision boundaries
  • SVM - Out of box supervised learning technique
  • Feature Space - Finite dimensional vector space
  • Each dimension represents feature
  • Goal of SVN - Train a model that assigns unseen objects into particular category
  • Creates linear partition of feature space
  • Based on features it places above or below separation linear
  • No stochastic element involved (No involvement of any previous state status)
  • support vector classifiers or soft margin classifiers - allows some observations to be on in-correct side of hyperplane allowing soft margin
Advantage
  • High Dimensionality, Memory Efficiency, Versatility
Disadvantages
  • Non probabilistic
More Reads

Happy Learning!!!
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