SVM Kernel Functions.

Kernel functions for Support Vector Machines that compute similarity in (possibly infinite) feature spaces via the kernel trick.

Available kernels

  • LinearKernel: Standard dot product.
  • PolynomialKernel: Polynomial transformation of dot product.
  • RBFKernel: Radial Basis Function (Gaussian) kernel.
  • SigmoidKernel: Hyperbolic tangent kernel.
  • StringKernel: Subsequence matching for text data.
  • PrecomputedKernel: Uses a user-provided kernel matrix.

Modules