Estimating the Generalization Performance of an SVM Efficiently
This paper proposes and analyzes an e cient and e ective approach for estimating the generalization performance of a support vector machine (SVM) for text classi cation. Without any computation-intensive resampling, the new estimators are computationally much more e cient 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 performancemeasures needed for evaluating text classi ers. 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 e ectively estimate the performance of SVM text classi ers in an e cient way.