Properties of Support Vector Machines

Neural Computation

Volumn 10, Issue 4, 1998, Pages 955-974

  Pontil, Massimiliano ^a   Verri, Alessandro ^b  

^a INFM   (Italy)

^b UNIVERSITY OF GENOA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; MATHEMATICS; NONLINEAR SYSTEM; STATISTICAL MODEL;

ARTIFICIAL INTELLIGENCE; LINEAR MODELS; MATHEMATICS; NONLINEAR DYNAMICS; PATTERN RECOGNITION, AUTOMATED;

EID: 0032523506     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976698300017575     Document Type: Article

References (9)

1. Bazaraa, M., & Shetty, C. M. (1979). Nonlinear programming New York: Wiley.
2. Blanz, V., Schölkopf, B., Bulthoff, H., Burges, C., Vapnik, V. N., & Vetter, T. (1996). Comparison of view-based object recognition algorithms using realistic 3D models. In Proc of ICANN'96, LNCS (Vol. 1112, pp. 251-256).
3. Cortes, C., & Vapnik, V. N. (1995). Support vector network. Machine Learning, 20, 1-25.
4. Courant, R., & Hilbert, D. (1959). Methods of mathematical physics. New York: Wiley.
5. Osuna, E., Freund, R., & Girosi, F. (1997). Training support vector machines: An application to face detection. In Proceedings of the International Conference on Computer Vision and Pattern Recognition.
6. Schölkopf, B., Sung, K., Burges, C., Girosi, F., Niyogi, P., Poggio, T., & Vapnik, V. N. (1996). Comparing support vector machines with gaussian kernels to radial basis function classifiers (AI Memo No. 1599). Cambridge, MA: Massachusetts Institute of Technology.
7. Vapnik, V. N. (1982). Estimation of dependencies based on empirical data. New York: Springer-Verlag.
8. Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer-Verlag.
9. Vapnik, V. N., & Chervonenkis, A. Ja. (1971). On the uniform convergence of relative frequencies of events to their probabilities. Theory Probab. Appl., 16, 264-280.