What does SVM do to solve the linear inseparable problem:

University of Michigan - Ann Arbor

The kernel function in an SVM model calculates the similarity in the new feature space of two points in the original feature space.

The SVM Gaussian kernel function is more complicated than the linear kernel function model, and it is easy to overfit.

SVM Kernel Function

- Radial Basis Function (RBF) Kernel
- Polynomial Kernel

Popular SVM Kernel Functions

The kernel function does not transform the original data points to the new high dimensional feature space. Instead, the kernelized SVM can compute these more complex decision boundaries just in terms of similarity calculations between pairs of points in the high dimensional space where the transformed feature representation is implicit.

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