Bound on the Leave-One-Out Error for 2-Class Classification using nu-SVMs
2001
Technical Report
ei
Three estimates of the leave-one-out error for $nu$-support vector (SV) machine binary classifiers are presented. Two of the estimates are based on the geometrical concept of the {em span}, which was introduced in the context of bounding the leave-one-out error for $C$-SV machine binary classifiers, while the third is based on optimisation over the criterion used to train the $nu$-support vector classifier. It is shown that the estimates presented herein provide informative and efficient approximations of the generalisation behaviour, in both a toy example and benchmark data sets. The proof strategies in the $nu$-SV context are also compared with those used to derive leave-one-out error estimates in the $C$-SV case.
Author(s): | Gretton, A. and Herbrich, R. and Schölkopf, B. and Rayner, PJW. |
Year: | 2001 |
Day: | 0 |
Department(s): | Empirical Inference |
Bibtex Type: | Technical Report (techreport) |
Institution: | University of Cambridge |
Digital: | 0 |
Note: | Updated May 2003 (literature review expanded) |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
PostScript
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BibTex @techreport{1854, title = {Bound on the Leave-One-Out Error for 2-Class Classification using $nu$-{SVM}s}, author = {Gretton, A. and Herbrich, R. and Sch{\"o}lkopf, B. and Rayner, PJW.}, organization = {Max-Planck-Gesellschaft}, institution = {University of Cambridge}, school = {Biologische Kybernetik}, year = {2001}, note = {Updated May 2003 (literature review expanded)}, doi = {} } |