Datensatz

Zusammenfassung

We define notions of stability for learning algorithms and show how to use these notions to derive generalization error bounds
based on the empirical error and the leave-one-out error. The
methods we use can be applied in the regression framework as well
as in the classification one when the classifier is obtained by
thresholding a real-valued function. We study the stability
properties of large classes of learning algorithms such as
regularization based algorithms. In particular we focus on Hilbert
space regularization and Kullback-Leibler regularization. We
demonstrate how to apply the results to SVM for regression and
classification.