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https://link.springer.com/article/10.1007%2FBF00994018
Sep 01, 1995 · The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated.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
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 a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine ...Cited by: 38765
http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.9362
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): 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 a linear decision surface is constructed.
https://dl.acm.org/doi/10.1023/A%3A1022627411411
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 a linear decision surface is constructed.Author: CortesCorinna, VapnikVladimir
https://en.wikipedia.org/wiki/Vladimir_N._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, …
https://paperity.org/p/7427560/support-vector-networks
Support-vector networks Support-vector networks. ... CORINNA CORTES VLADIMIR VAPNIK AT T Bell Labs. Holmdel NJ USA Lorenza Saitta 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 highdimension ...Cited by: 38765
https://www.semanticscholar.org/paper/Support-vector-networks-Cortes-Vapnik/24e6cf0796237f21c780a3f0c996817f57b3a1bd
The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.High generalization ability of support-vector networks utilizing polynomial input transformations is …
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.294.7243
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract. The support-vector network is a new leaming machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very highdimension feature space. In this feature space a linear decision surface is constructed.
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