The support vector method

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1327, Issue , 1997, Pages 264-271

  Vapnik, Vladimir N ^a  

^a AT AND T LABS RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL OPERATORS; NEURAL NETWORKS; PATTERN RECOGNITION; PROBABILITY DENSITY FUNCTION;

CLASSICAL STATISTICS; DENSITY ESTIMATION; FUNCTION ESTIMATION; GENERALIZATION ABILITY; INFINITE NUMBERS; LINEAR OPERATOR EQUATIONS; REGRESSION ESTIMATION; SUPPORT VECTOR METHOD;

VECTOR SPACES;

EID: 84956666701     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0020166     Document Type: Conference Paper

References (6)

1. B.E. Boser, I.M. Guyon, and V.N. Vapnik. A training algorithm for optimal margin classifier. Proceedings of the 5th Annual A CM Workshop on Computational Learning Theory. pp 144-152, Pittsburgh, PA, 1992.
2. C. Cortes and V. Vapnik. Support Vector Network. Machine Learning. 20:273-297, 1995.
3. H. Drucker, C.J. Burges, L. Kaufman, A. Smola, and V. Vapnik. Support vector regression machines. In Advances in Neural Information Processing Systems 9, 1997, MIT Press.
4. V. Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, 1995, New-York.
5. V.N. Vapnik and A. Ya. Chervonenkis. Theory of Pattern Recognition (in Russian) Nauka, Moscow, 1974. German translation: W.N. Wapnik, A Ja. Tscherwonenkis Teorie der Zeichenerkennung, Akademia, Berlin, 1979.
6. V. Vapnik, S. Golowich, and A. Smola. Support vector method for function approximation, regression estimation and signal processing. In Advances in Neural Information Processing Systems, 9., 1997 MIT Press.