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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.Cited by: 38765
http://scholar.google.com/citations?user=vtegaJgAAAAJ&hl=en
C Cortes, V Vapnik. Machine learning 20 (3), 273-297, 1995. 39033: 1995: A training algorithm for optimal margin classifiers. BE Boser, IM Guyon, VN Vapnik. ... Support vector method for function approximation, regression estimation and signal processing. V Vapnik, SE Golowich, AJ Smola.
https://en.wikipedia.org/wiki/Vapnik%27s_principle
At the end of 1990, Vladimir Vapnik moved to the USA and joined the Adaptive Systems Research Department at AT&T Bell Labs in Holmdel, New Jersey. While at AT&T, Vapnik and his colleagues did work on the support-vector machine.Alma mater: Institute of Control Sciences, …
https://www.sciencedirect.com/topics/neuroscience/support-vector-machines
Trait values for unknown individuals are then assessed relative to this function and classified accordingly (Cortes and Vapnik, 1995). Support vector machines are designed for pairwise comparisons, but recent work has demonstrated that they can effectively estimate ancestry between more than two groups ...
https://www.scirp.org/reference/ReferencesPapers.aspx?ReferenceID=2235455
Cortes, C. and Vapnik, V. (1995) Support-Vector Networks. Machine Learning, 20, 273-297. ... Over the years, many researchers have used support vector regression (SVR) quite successfully to conquer this challenge. In this paper, an SVR based forecasting model is proposed which first uses the principal component analysis (PCA) to extract the low ...
http://www.sciepub.com/reference/47107
Cortes, C. and Vapnik, V., “Support-Vector Networks, ... This research aims to assess and compare performance of single and ensemble classifiers of Support Vector Machine (SVM) and Classification Tree (CT) by using simulation data. The simulation data is based on three data structures which are linearly separable, linearly nonseparable and ...
http://citeseerx.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
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