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pro vyhledávání: '"Gérard Biau"'
Pour qui souhaite découvrir des concepts mathématiques indispensables à la modélisation des phénomènes naturels, ce livre scientifique apparaît comme une référence. Sans excès de théorie, on a le droit au coeur de cet ouvrage à une prése
Autor:
Gérard Biau, Benoît Cadre
Publikováno v:
Advances in Contemporary Statistics and Econometrics ISBN: 9783030732486
Gradient boosting is a state-of-the-art prediction technique that sequentially produces a model in the form of linear combinations of elementary predictors—typically decision trees—by solving an infinite-dimensional convex optimization problem. W
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3e694c6bfdb68ad9878da189cfdfe850
https://doi.org/10.1007/978-3-030-73249-3_2
https://doi.org/10.1007/978-3-030-73249-3_2
Publikováno v:
Sankhya A. 81:347-386
Given an ensemble of randomized regression trees, it is possible to restructure them as a collection of multilayered neural networks with particular connection weights. Following this principle, we reformulate the random forest method of Breiman (200
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::485d3a0f4b83c05b4fe29155a1acc271
https://doi.org/10.1007/s13171-018-0133-y
https://doi.org/10.1007/s13171-018-0133-y
Publikováno v:
Machine Learning
Machine Learning, Springer Verlag, 2019, 108 (6), pp.971-992. ⟨10.1007/s10994-019-05787-1⟩
Machine Learning, 2019, 108 (6), pp.971-992. ⟨10.1007/s10994-019-05787-1⟩
Machine Learning, Springer Verlag, 2019, 108 (6), pp.971-992. ⟨10.1007/s10994-019-05787-1⟩
Machine Learning, 2019, 108 (6), pp.971-992. ⟨10.1007/s10994-019-05787-1⟩
International audience; Gradient tree boosting is a prediction algorithm that sequentially produces a model in the form of linear combinations of decision trees, by solving an infinite-dimensional optimization problem. We combine gradient boosting an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::812cda98fa324f2a46483a53d3b28f15
https://hal.sorbonne-universite.fr/hal-01723843
https://hal.sorbonne-universite.fr/hal-01723843
Publikováno v:
Electron. J. Statist. 15, no. 1 (2021), 427-505
State-of-the-art learning algorithms, such as random forests or neural networks, are often qualified as “black-boxes” because of the high number and complexity of operations involved in their prediction mechanism. This lack of interpretability is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c83c39f2ee43b54aa52906e9fce390ca
Autor:
Gérard Biau, Erwan Scornet
Publikováno v:
TEST. 25:264-268
First of all, we would like to thank S. Arlot, P. Buhlmann, R. Genuer, P. Geurts, G. Hooker, F. Leonardi, L. Mentch, S. Wager, and L.Wehenkel for their insightful and stimulating comments on our review paper, as well as for their thorough investigati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f985354b319e32d406fbb60a5a56923f
https://doi.org/10.1007/s11749-016-0488-0
https://doi.org/10.1007/s11749-016-0488-0
Publikováno v:
Annals of Statistics
Annals of Statistics, Institute of Mathematical Statistics, 2020, 48 (3), pp.1539-1566. ⟨10.1214/19-AOS1858⟩
Ann. Statist. 48, no. 3 (2020), 1539-1566
Annals of Statistics, 2020, 48 (3), pp.1539-1566. ⟨10.1214/19-AOS1858⟩
Annals of Statistics, Institute of Mathematical Statistics, 2020, 48 (3), pp.1539-1566. ⟨10.1214/19-AOS1858⟩
Ann. Statist. 48, no. 3 (2020), 1539-1566
Annals of Statistics, 2020, 48 (3), pp.1539-1566. ⟨10.1214/19-AOS1858⟩
International audience; Generative Adversarial Networks (GANs) are a class of generative algorithms that have been shown to produce state-of-the art samples, especially in the domain of image creation. The fundamental principle of GANs is to approxim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3264bd66ea91babed2f9f838836ea965
Autor:
Andreas Ziegler, Yufeng Liu, Gérard Biau, Michael Kohler, James D. Malley, Jochen Kruppa, Inke R. König
Publikováno v:
Biometrical Journal. 56:534-563
Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::be9704a3978646d79ddbabe527d3f1c1
https://doi.org/10.1002/bimj.201300068
https://doi.org/10.1002/bimj.201300068
Autor:
Gérard Biau, Luc Devroye
This text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now in one self-contained volume, this book systematically covers key statistical, probabilistic, combinator
Publikováno v:
Electronic Journal of Statistics
Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2016, 10, pp.2097-2123
Electronic Journal of Statistics, 2016, 10, pp.2097-2123. ⟨10.1214/16-EJS1164⟩
Electron. J. Statist. 10, no. 2 (2016), 2097-2123
Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2016, 10, pp.2097-2123. ⟨10.1214/16-EJS1164⟩
Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2016, 10, pp.2097-2123
Electronic Journal of Statistics, 2016, 10, pp.2097-2123. ⟨10.1214/16-EJS1164⟩
Electron. J. Statist. 10, no. 2 (2016), 2097-2123
Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2016, 10, pp.2097-2123. ⟨10.1214/16-EJS1164⟩
International audience; The detection of change-points in a spatially or time ordered data sequence is an important problem in many fields such as genetics and finance. We derive the asymptotic distribution of a statistic recently suggested for detec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f33219852cbd81e339d6e3a2a49d3459
https://hal.archives-ouvertes.fr/hal-01490927
https://hal.archives-ouvertes.fr/hal-01490927