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What are Different Kernels in SVM?

Answer»

There are six types of kernels in SVM:

  • Linear kernel - used when data is linearly separable. 
  • POLYNOMIAL kernel - When you have DISCRETE data that has no natural notion of smoothness.
  • Radial basis kernel - CREATE a decision boundary able to do a much better job of separating two classes than the linear kernel.
  • Sigmoid kernel - used as an activation FUNCTION for NEURAL networks.


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