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https://link.springer.com/article/10.1007%2FBF00994018
Sep 01, 1995 · High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a …Cited by: 38765
http://image.diku.dk/imagecanon/material/cortes_vapnik95.pdf
Support-Vector Networks CORINNA CORTES [email protected] VLADIMIR VAPNIK [email protected] AT&T Bell Labs., Holmdel, NJ 07733, USA Editor: Lorenza Saitta Abstract. The support-vector network is a new learning machine for two-group classification problems. The
https://link.springer.com/article/10.1023%2FA%3A1022627411411
Sep 01, 1995 · The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space …Cited by: 38765
https://en.wikipedia.org/wiki/Vladimir_Vapnik
Vladimir Naumovich Vapnik (Russian: Владимир Наумович Вапник; born 6 December 1936) is one of the main developers of the Vapnik–Chervonenkis theory of statistical learning, and the co-inventor of the support-vector machine method, and support-vector clustering algorithm.Alma mater: Institute of Control Sciences, …
http://scholar.google.com/citations?user=vtegaJgAAAAJ&hl=en
Support-vector networks. C Cortes, V Vapnik. Machine learning 20 (3), 273-297, 1995. ... CJC Burges, L Kaufman, AJ Smola, V Vapnik. Advances in neural information processing systems, 155-161, 1997. 3176: 1997: Support vector method for function approximation, regression estimation and signal processing ... Journal of machine learning research 2 ...
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