How can I control the overfiting in the logistic, nearest neighbors and SVM classifiers?
If I were using a neural network, I could use early stopping to avoid overfitting. In the case of SVM I think the procedure is to control the value of C. An small value of C would help the generalization and avoid overfit ( is this correct?)
But suppose I have one-nearest-neighbor(1-NN) and one dataset. How can I control the overfit? There is no parameters to chose. The same question go to the logistic classifier. I can try different partitions folds in the cross-validation to verify the performance and check if the overfitting occured but how can I avoid it?
asked Jul 03 '13 at 12:59