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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
This "Cited by" count includes citations to the following articles in Scholar. ... Support-vector networks. 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.
https://www.scirp.org/reference/ReferencesPapers.aspx?ReferenceID=2235455
Cortes, C. and Vapnik, V. (1995) Support-Vector Networks. Machine Learning, 20, 273-297. Scientific Research An Academic Publisher ... Term Financial Time Series Forecasting Integrating Principal Component Analysis and Independent Component Analysis with Support Vector Regression.
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.
http://homepages.rpi.edu/~bennek/class/mmld/papers/svn.pdf
output from the 4 hidden units weights of the 4 hidden units dot−products weights of the 5 hidden units dot−products dot−product perceptron output
https://en.wikipedia.org/wiki/Vapnik%27s_principle
Vladimir Vapnik was born in the Soviet Union. ... In 2000, Vapnik and neural networks expert, Hava Siegelmann developed Support-Vector Clustering, which enabled the algorithm to categorize inputs without labels - becoming one of the most ubiquitous data clustering applications in use.Alma mater: Institute of Control Sciences, …
https://www.semanticscholar.org/paper/Support-Vector-Networks-Cortes-Vapnik/52b7bf3ba59b31f362aa07f957f1543a29a4279e
Corinna Cortes, V. Vapnik 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.
http://helios.mi.parisdescartes.fr/~bouzy/Doc/AA1/CortesVapnik-SupportVectorNetworks-ML1995.pdf
Support-vector networks Reference • These slides present the following paper: – C.Cortes, V.Vapnik, « support vector networks », Machine Learning (1995) • They are commented with my personal view to teach the key ideas of SVN. • The outline mostly follows the outline of the paper.
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