I'm currently using a SVM (classification) to predict the outcome of a sports match. I split the data into three sets, a training, cross-validation, and test set. I have a total of 2200 sample points. I optimised a regularisation parameter by looping through a variety of regularisation parameter values and then testing its accuracy on the cross-validation set.
I had a 72.2% accuracy on the training set, 65.2% on the CV set, and 63.4% on the test set. This appears to me as overfitting, but I'm not sure how to counteract it on an SVM. I could possibly remove features, maybe select the best K parameters, but does anyone else have any ideas?