Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Suykens, Johan AK"'
In this paper, we provide a precise characterization of generalization properties of high dimensional kernel ridge regression across the under- and over-parameterized regimes, depending on whether the number of training data n exceeds the feature dim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f81f0f86e4df9ab1b7f0235928fe19e
http://arxiv.org/abs/2010.02681
http://arxiv.org/abs/2010.02681
ispartof: CoRR vol:abs/2006.00247
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1131::ce9da5e59eb90510a0f4421ebf2d4f2b
https://lirias.kuleuven.be/handle/123456789/675905
https://lirias.kuleuven.be/handle/123456789/675905
Autor:
Schreurs, Joachim, Vranckx, Iwein, Ketelaere, Bart De, Hubert, Mia, Suykens, Johan AK, Rousseeuw, Peter J
Publikováno v:
Statistics and Computing
The minimum regularized covariance determinant method (MRCD) is a robust estimator for multivariate location and scatter, which detects outliers by fitting a robust covariance matrix to the data. Its regularization ensures that the covariance matrix
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::414615ad555cbbb2b5f461c482b80ab7
ispartof: CoRR vol:abs/2006.01073
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1131::a26876d5fda9a77adc628cb6f066a48d
https://lirias.kuleuven.be/handle/20.500.12942/701890
https://lirias.kuleuven.be/handle/20.500.12942/701890
ispartof: JOURNAL OF MACHINE LEARNING RESEARCH vol:20 status: published
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1131::2499c2b213d110e03ee3defb23c25490
https://lirias.kuleuven.be/handle/123456789/652296
https://lirias.kuleuven.be/handle/123456789/652296
Autor:
De Moor Bart, Suykens Johan AK, Tranchevent Leon-Charles, Daemen Anneleen, Falck Tillmann, Yu Shi, Moreau Yves
Publikováno v:
BMC Bioinformatics, Vol 11, Iss 1, p 309 (2010)
Abstract Background This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as L∞
Externí odkaz:
https://doaj.org/article/f84b1b3d0c724a01b813660709527da4
Autor:
Yu S; Bioinformatics Group, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee B-3001, Belgium. shee.yu@gmail.com, Falck T, Daemen A, Tranchevent LC, Suykens JA, De Moor B, Moreau Y
Publikováno v:
BMC bioinformatics [BMC Bioinformatics] 2010 Jun 08; Vol. 11, pp. 309. Date of Electronic Publication: 2010 Jun 08.
Autor:
Daemen A; Department of Electrical Engineering (ESAT-SCD), Katholieke Universiteit Leuven, Kasteelpark Arenberg, 3001 Leuven, Belgium. anneleen.daemen@esat.kuleuven.be., Gevaert O, Ojeda F, Debucquoy A, Suykens JA, Sempoux C, Machiels JP, Haustermans K, De Moor B
Publikováno v:
Genome medicine [Genome Med] 2009 Apr 03; Vol. 1 (4), pp. 39. Date of Electronic Publication: 2009 Apr 03.