What is the typical number of kernel evaluations (between two training vectors) performed during a (kernelized) Support Vector Machine (SVM) training?
I am asking this question because I need to determine how much I need to optimize the kernel in order to have some hope to do the training in a reasonable amount of time. The current kernel calculation time is prohibitive (1+ hour), but there is room for improvement (by many orders of magnitude).
Note: There are about 60k vectors in one class and 10k vectors in the other one.