As is known to all, SVM can use kernel method to project data points in higher spaces so that points can be separated by a linear space.
But we can also use logistic regression to choose this boundary in the kernel space, so what's the advantages of SVM?
Since SVM uses a sparse model in which only those support vectors make contributions when predicting, does this make SVM faster in prediction?
Solved – Kernel logistic regression vs SVM
logisticsvm
Best Answer
KLRs and SVMs
Looking at the above it almost feels like kernel logistic regression is what you should be using. However, there are certain advantages that SVMs enjoy