%0 Conference Paper %1 VapCha00 %A Vapnik, V. %A Chapelle, O. %B Advances in Large Margin Classifiers %C Cambridge, MA %D 2000 %E Smola, A.J. %E Bartlett, P.L. %E Schölkopf, B. %E Schuurmans, D. %I MIT Press %K imported %P 261-280 %T Bounds on error expectation for SVM %X Bounds on the Error Expectation for SVM in terms of the leave-one-out estimate and the expected value of certain properties of the SVM are given. It is shown that previous bounds involving the minimum margin and the diameter $D$ of the set of support vectors can be improved by the replacement of $D^2$ by $SD$. Here, $S$ is a new geometric property of the support vectors called the span. Experimental results show that this improvement gives significantly better predictions of test error than the previous bounds, and seems likely to be useful for model selection.

@inproceedings{VapCha00, abstract = {Bounds on the Error Expectation for SVM in terms of the leave-one-out estimate and the expected value of certain properties of the SVM are given. It is shown that previous bounds involving the minimum margin and the diameter $D$ of the set of support vectors can be improved by the replacement of $D^2$ by $SD$. Here, $S$ is a new geometric property of the support vectors called the span. Experimental results show that this improvement gives significantly better predictions of test error than the previous bounds, and seems likely to be useful for model selection.}, added-at = {2008-04-30T12:59:47.000+0200}, address = {Cambridge, MA}, author = {Vapnik, V. and Chapelle, O.}, biburl = {https://www.bibsonomy.org/bibtex/2873d55324d9ad4d595e617e8a2603647/kdubiq}, booktitle = {Advances in Large Margin Classifiers}, description = {KDubiq Blueprint}, editor = {Smola, A.J. and Bartlett, P.L. and Sch{\"o}lkopf, B. and Schuurmans, D.}, groupsearch = {0}, interhash = {eb4f71d7942aecfa35b5c79f31dabd0b}, intrahash = {873d55324d9ad4d595e617e8a2603647}, keywords = {imported}, pages = {261-280}, publisher = {{MIT} Press}, timestamp = {2008-04-30T13:00:30.000+0200}, title = {Bounds on error expectation for {SVM}}, year = 2000 }