I'm using libsvm in C-SVC mode with a polynomial kernel of degree 2 and I'm required to train multiple SVMs. Each training set has 10 features and 5000 vectors. During training, I am getting this warning for most of the SVMs that I train:
WARNING: reaching max number of iterations optimization finished, #iter = 10000000
Could someone please explain what does this warning implies and, perhaps, how to avoid it?
I also want to apply cross-validation for my models in order to determine the best choices for gamma and C (regularization). My plan is to just try every combinations of these 10 values: 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000 for both parameters and see which combination produces the best accuracy during cross-validation. Is this enough? Should I use more values in this interval, or should I choose a wider interval?