I'm comparing the results of an Newton optimizer for a modified version of SVM ( a generalized quadratic loss, similar to the one stated in:
) with classic SVM^light for regression. The problem is that it's able to overfit the data (UCI Yacht data-set) but I can't reach the generalization results of SVM^light. I've tried several hyper-parameters grids. I'm solving the primal problem. I'll send you my code if you need it. Any suggestion?