I'm training a bunch of SVM models to do one-against-all multiclass classification (a test instance is classified as the class which produces the largest positive SVM response).
What's the best way to do cross-validation for selection of the regularization parameter?
Should I do cross-validation separately for each SVM model I train, potentially getting a different regularization parameter for each of the models? Or should I cross-validate as a group, where I try a particular regularization parameter across all models?
Also, what is a good metric for cross-validation? Accuracy? Precision? F-Measure?