Where is it best to use svm with linear kernel?

machine-learning classification svm mathematical-optimization
Anna.Klee picture Anna.Klee · Dec 13, 2013 · Viewed 20k times · Source

I am currently studing svm and was wondering what the application of svm`s with linear kernel is. In my opinion it must be something applied to solving a linear optimization problem. Is this correct?

I appreciate your answer!

Answer

lennon310 picture lennon310 · Dec 13, 2013

One more thing to add: linear SVM is less prone to overfitting than non-linear. And you need to decide which kernel to choose based on your situation: if your number of features is really large compared to the training sample, just use linear kernel; if your number of features is small, but the training sample is large, you may also need linear kernel but try to add more features; if your feature number is small (10^0 - 10^3), and the sample number is intermediate (10^1 - 10^4), use Gaussian kernel will be better.

As far as I know, SVM with linear kernel is usually comparable with logistic regression .

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