Estimating the Generalization Performance of a SVM Efficiently

This paper proposes and analyzes an efficient and effective approach for estimating the generalization performance of a support vector machine (SVM) for text classification. Without any computation-intensive resampling, the new estimators are computationally much more efficient than cross-validation or bootstrapping. They can be computed at essentially no extra cost immediately after training a single SVM. Moreover, the estimators developed here address the special performance measures needed for evaluating text classifiers. They can be used not only to estimate the error rate, but also to estimate recall, precision, and F₁. A theoretical analysis and experiments show that the new method can effectively estimate the performance of SVM text classifiers in an efficient way.