Bounds on error expectation for support vector machines

Neural Computation

Volumn 12, Issue 9, 2000, Pages 2013-2036

  Vapnik, V ^a   Chapelle, O ^a  

^a UNIVERSITÉ DE LYON   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; LEARNING;

LEARNING; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED;

EID: 0034264380     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976600300015042     Document Type: Article

References (8)

1. Bartlett, P., & Shawe-Taylor, J. (1999). Generalization performance of support vector machines and other pattern classifiers. In B. Schölkopf, C. J. C. Burges, & A. J. Smola, (Eds.), Advances in kernel methods - Support vector learning (pp. 43-54). Cambridge, MA. MIT Press.
2. Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In D. Haussler, (Ed.), Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory (pp. 144-152). Pittsburgh, PA: ACM Press.
3. Luntz, A., & Brailovsky, V. (1969). On estimation of characters obtained in statistical procedure of recognition. Technicheskaya Kibernetica, 3 (in Russian).
4. Schölkopf, B., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (1999). Generalization bounds via eigenvalues of the Gram matrix (Tech. Rep. No. 99-035). NeuroCOLT.
5. Shawe-Taylor, J., Bartlett, P. L., Williamson, R. C., & Anthony, M. (1998). Structural risk minimization over data-dependent hierarchies. IEEE Transactions on Information Theory.
6. Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer-Verlag.
7. Vapnik, V. (1998). Statistical learning theory. New York: Wiley.
8. Weston, J. (1999). Leave-one-out machines. In M. Press (Ed.), Advances in large margin classifiers.