The Citing articles tool gives a list of articles citing the current article. The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program . You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).
Cited article:
Stéphane Boucheron , Olivier Bousquet , Gábor Lugosi
ESAIM: PS, 9 (2005) 323-375
Published online: 2005-11-15
This article has been cited by the following article(s):
231 articles | Pages:
Ranking and Empirical Minimization of U-statistics
Stéphan Clémençon, Gábor Lugosi and Nicolas Vayatis The Annals of Statistics 36 (2) (2008) https://doi.org/10.1214/009052607000000910
HECTAR: A method to predict subcellular targeting in heterokonts
Bernhard Gschloessl, Yann Guermeur and J Mark Cock BMC Bioinformatics 9 (1) (2008) https://doi.org/10.1186/1471-2105-9-393
Bayesian approach, theory of empirical risk minimization. Comparative analysis
I. V. Sergienko, A. M. Gupal and A. A. Vagis Cybernetics and Systems Analysis 44 (6) 822 (2008) https://doi.org/10.1007/s10559-008-9058-0
PAC-Bayesian bounds for randomized empirical risk minimizers
P. Alquier Mathematical Methods of Statistics 17 (4) 279 (2008) https://doi.org/10.3103/S1066530708040017
Constructing processes with prescribed mixing coefficients
Leonid (Aryeh) Kontorovich Statistics & Probability Letters 78 (17) 2910 (2008) https://doi.org/10.1016/j.spl.2008.04.016
Lower Bounds for the Empirical Minimization Algorithm
Shahar Mendelson IEEE Transactions on Information Theory 54 (8) 3797 (2008) https://doi.org/10.1109/TIT.2008.926323
Maria-Florina Balcan, Avrim Blum and Santosh Vempala 671 (2008) https://doi.org/10.1145/1374376.1374474
Kernel methods in machine learning
Thomas Hofmann, Bernhard Schölkopf and Alexander J. Smola The Annals of Statistics 36 (3) (2008) https://doi.org/10.1214/009053607000000677
Reducing mechanism design to algorithm design via machine learning
Maria-Florina Balcan, Avrim Blum, Jason D. Hartline and Yishay Mansour Journal of Computer and System Sciences 74 (8) 1245 (2008) https://doi.org/10.1016/j.jcss.2007.08.002
Learning by mirror averaging
A. Juditsky, P. Rigollet and A. B. Tsybakov The Annals of Statistics 36 (5) (2008) https://doi.org/10.1214/07-AOS546
Christopher P. Diehl and Ashley J. Llorens 468 (2008) https://doi.org/10.1109/MLSP.2008.4685525
Obtaining fast error rates in nonconvex situations
Shahar Mendelson Journal of Complexity 24 (3) 380 (2008) https://doi.org/10.1016/j.jco.2007.09.001
Optimal rates of aggregation in classification under low noise assumption
Guillaume Lecué Bernoulli 13 (4) (2007) https://doi.org/10.3150/07-BEJ6044
Learning Theory
Guillaume Lecué Lecture Notes in Computer Science, Learning Theory 4539 142 (2007) https://doi.org/10.1007/978-3-540-72927-3_12
Simultaneous adaptation to the margin and to complexity in classification
Guillaume Lecué The Annals of Statistics 35 (4) (2007) https://doi.org/10.1214/009053607000000055
Multi-kernel regularized classifiers
Qiang Wu, Yiming Ying and Ding-Xuan Zhou Journal of Complexity 23 (1) 108 (2007) https://doi.org/10.1016/j.jco.2006.06.007
On regularization algorithms in learning theory
Frank Bauer, Sergei Pereverzev and Lorenzo Rosasco Journal of Complexity 23 (1) 52 (2007) https://doi.org/10.1016/j.jco.2006.07.001
Fast learning rates for plug-in classifiers
Jean-Yves Audibert and Alexandre B. Tsybakov The Annals of Statistics 35 (2) (2007) https://doi.org/10.1214/009053606000001217
Guest editorial: Learning theory
Olivier Bousquet and André Elisseeff Machine Learning 66 (2-3) 115 (2007) https://doi.org/10.1007/s10994-007-0753-2
Generalized mirror averaging and D-convex aggregation
K. Lounici Mathematical Methods of Statistics 16 (3) 246 (2007) https://doi.org/10.3103/S1066530707030040
Oracle inequalities for multi-fold cross validation
Aad W. van der Vaart, Sandrine Dudoit and Mark J. van der Laan Statistics & Decisions 24 (3) 351 (2006) https://doi.org/10.1524/stnd.2006.24.3.351
Learning Theory
Guillaume Lecué Lecture Notes in Computer Science, Learning Theory 4005 364 (2006) https://doi.org/10.1007/11776420_28
Categorization
Marcin Peski SSRN Electronic Journal (2006) https://doi.org/10.2139/ssrn.884232
On the Kernel Rule for Function Classification
C. Abraham, G. Biau and B. Cadre Annals of the Institute of Statistical Mathematics 58 (3) 619 (2006) https://doi.org/10.1007/s10463-006-0032-1
Classification with reject option
Radu Herbei and Marten H. Wegkamp Canadian Journal of Statistics 34 (4) 709 (2006) https://doi.org/10.1002/cjs.5550340410
Statistical inference on graphs
Gérard Biau and Kevin Bleakley Statistics & Decisions 24 (2) 209 (2006) https://doi.org/10.1524/stnd.2006.24.2.209
Learning Theory
Magalie Fromont and Christine Tuleau Lecture Notes in Computer Science, Learning Theory 4005 94 (2006) https://doi.org/10.1007/11776420_10
Learning Theory
Maria-Florina Balcan and Avrim Blum Lecture Notes in Computer Science, Learning Theory 3559 111 (2005) https://doi.org/10.1007/11503415_8
Functional Classification in Hilbert Spaces
G. Biau, F. Bunea and M.H. Wegkamp IEEE Transactions on Information Theory 51 (6) 2163 (2005) https://doi.org/10.1109/TIT.2005.847705
Machine Learning and Data Mining in Pattern Recognition
Ichigaku Takigawa, Mineichi Kudo and Atsuyoshi Nakamura Lecture Notes in Computer Science, Machine Learning and Data Mining in Pattern Recognition 3587 90 (2005) https://doi.org/10.1007/11510888_10
Learning Theory
Stéphan Clémençon, Gábor Lugosi and Nicolas Vayatis Lecture Notes in Computer Science, Learning Theory 3559 1 (2005) https://doi.org/10.1007/11503415_1
Pages:
201 to 231 of 231 articles