FAQ: Support Vector Machines - Radial Bias Function Kernel

This community-built FAQ covers the “Radial Bias Function Kernel” exercise from the lesson “Support Vector Machines”.

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FAQs on the exercise Radial Bias Function Kernel

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Concerning rbf kernel: If we have kernel gamma parameter, which defines the margins why, should we use ‘C’ as the additional parameter?


I still don’t quite understand it, but it seems that C and gamma have different roles. According to a documentation, it says:

When training an SVM with the Radial Basis Function (RBF) kernel, two parameters must be considered: C and gamma . The parameter C , common to all SVM kernels, trades off misclassification of training examples against simplicity of the decision surface. A low C makes the decision surface smooth, while a high C aims at classifying all training examples correctly. gamma defines how much influence a single training example has. The larger gamma is, the closer other examples must be to be affected.

Also, another documentation says:

Intuitively, the gamma parameter defines how far the influence of a single training example reaches, with low values meaning ‘far’ and high values meaning ‘close’. The gamma parameters can be seen as the inverse of the radius of influence of samples selected by the model as support vectors.

The C parameter trades off correct classification of training examples against maximization of the decision function’s margin. For larger values of C , a smaller margin will be accepted if the decision function is better at classifying all training points correctly. A lower C will encourage a larger margin, therefore a simpler decision function, at the cost of training accuracy. In other wordsC behaves as a regularization parameter in the SVM.