CODEWITHSUNDEEP
machine-learningartificial-intelligencesvm
CuriousMind
CuriousMind

Reputation: 15808

How to choose the right kernel functions

I have a very general question: how do I choose the right kernel function for SVM? I know the ultimate answer is try all the kernels, do out-of-sample validation, and pick the one with best classification result. But other than that, is there any guideline of trying the different kernel functions?

Upvotes: 7

Views: 12581

Answers (2)

Marc Claesen
Marc Claesen

Reputation: 17026

Always try the linear kernel first, simply because it's so much faster and can yield great results in many cases (specifically high dimensional problems).

If the linear kernel fails, in general your best bet is an RBF kernel. They are known to perform very well on a large variety of problems.

Upvotes: 5

storaged
storaged

Reputation: 1847

Look here to find the answer.

https://stats.stackexchange.com/questions/18030/how-to-select-kernel-for-svm

Basically, there is rather no one good path to choose, unless you know something important about your data that might determine proper kernel to use. However, follow the link above to get more specific information.

Upvotes: 2

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