Support vector machines with a reject option

Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Volumn , Issue , 2009, Pages 537-544

  Grandvalet, Yves ^a,b   Rakotomamonjy, Alain ^c   Keshet, Joseph ^b   Canu, Stéphane ^c  

^a UNIVERSITÉ DE TECHNOLOGIE DE COMPIÈGNE   (France)

^b IDIAP RESEARCH INSTITUTE   (Switzerland)

^c University of Rouen   (France)

Author keywords

[No Author keywords available]

Indexed keywords

QUADRATIC PROGRAMMING;

BAYES DECISION RULES; BINARY CLASSIFICATION; CONDITIONAL PROBABILITIES; KERNEL MACHINE; LOSS FUNCTIONS; OPTIMAL DECISION-RULE; POSTERIOR PROBABILITY; SIMPLE++; SUPPORT VECTORS MACHINE; THRESHOLD POINT;

SUPPORT VECTOR MACHINES;

EID: 70049110659     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. Bartlett, P. L., & Tewari, A. (2007). Sparseness vs estimating conditional probabilities: Some asymptotic results. Journal of Machine Learning Research, 8, 775-790. (Pubitemid 46677047)
2. Bartlett, P. L., & Wegkamp, M. H. (2008). Classification with a reject option using a hinge loss. Journal of Machine Learning Research, 9, 1823-1840.
3. Chow, C. K. (1970). On optimum recognition error and reject tradeoff. IEEE Trans. on Info. Theory, 16, 41-46.
4. Fumera, G., & Roli, F. (2002). Support vector machines with embedded reject option. Pattern Recognition with Support Vector Machines: First International Workshop (pp. 68-82). Springer.
5. Grandvalet, Y., Mariéthoz, J., & Bengio, S. (2006). A probabilistic interpretation of SVMs with an application to unbalanced classification. NIPS 18 (pp. 467-474). MIT Press.
6. Herbei, R., & Wegkamp, M. H. (2006). Classification with reject option. The Canadian Journal of Statistics, 34, 709-721.
7. Kwok, J. T. (1999). Moderating the outputs of support vector machine classifiers. IEEE Trans. on Neural Networks, 10, 1018-1031.
8. Steinwart, I. (2005). Consistency of support vector machine and other regularized kernel classifiers. IEEE Trans. on Info. Theory, 51, 128-142. (Pubitemid 40145991)
9. Vapnik, V. N. (1995). The nature of statistical learning theory. Springer Series in Statistics. Springer.
10. Vishwanathan, S. V. N., Smola, A., & Murty, N. (2003). SimpleSVM. Proceedings of the Twentieth International Conference on Machine Learning (pp. 68-82). AAAI.