@article{1439,
  title = {Stability and Generalization},
  journal = {Journal of Machine Learning Research},
  abstract = {
  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.},
  volume = {2},
  pages = {499-526},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2002},
  slug = {1439},
  author = {Bousquet, O. and Elisseeff, A.}
}