Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Fischer, Aurelie"'
Autor:
Delattre, Sylvain, Fischer, Aurélie
In this paper, we propose a method to build a sequence of generalized empirical principal curves, with selected length, so that, in Hausdor distance, the images of the estimating principal curves converge in probability to the image of g.
Externí odkaz:
http://arxiv.org/abs/1911.06728
Using a trimming approach, we investigate a k-means type method based on Bregman divergences for clustering data possibly corrupted with clutter noise. The main interest of Bregman divergences is that the standard Lloyd algorithm adapts to these dist
Externí odkaz:
http://arxiv.org/abs/1812.04356
Autor:
Fischer, Aurélie, Mougeot, Mathilde
In this paper, we introduce a new learning strategy based on a seminal idea of Mojirsheibani (1999, 2000, 2002a, 2002b), who proposed a smart method for combining several classifiers, relying on a consensus notion. In many aggregation methods, the pr
Externí odkaz:
http://arxiv.org/abs/1803.03166
Autor:
Fischer, Aurélie, Picard, Dominique
In this paper, we consider the estimation of a change-point for possibly high-dimensional data in a Gaussian model, using a k-means method. We prove that, up to a logarithmic term, this change-point estimator has a minimax rate of convergence. Then,
Externí odkaz:
http://arxiv.org/abs/1802.07617
Autor:
Delattre, Sylvain, Fischer, Aurélie
Principal curves are defined as parametric curves passing through the "middle" of a probability distribution in R^d. In addition to the original definition based on self-consistency, several points of view have been considered among which a least squ
Externí odkaz:
http://arxiv.org/abs/1707.01326
Akademický článek
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Publikováno v:
Wind Energy, Wiley, 2017, 20 (12), pp.2037 - 2047
We focus on wind power modeling using machine learning techniques. We show on real data provided by the wind energy company Ma{\"i}a Eolis, that parametric models, even following closely the physical equation relating wind production to wind speed ar
Externí odkaz:
http://arxiv.org/abs/1610.01000
Akademický článek
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This paper deals with the estimation of a probability measure on the real line from data observed with an additive noise. We are interested in rates of convergence for the Wasserstein metric of order $p\geq 1$. The distribution of the errors is assum
Externí odkaz:
http://arxiv.org/abs/1404.0646
Publikováno v:
Journal of Multivariate Analysis (2016), vol. 146, 18--28
A new method for combining several initial estimators of the regression function is introduced. Instead of building a linear or convex optimized combination over a collection of basic estimators $r_1,\dots,r_M$, we use them as a collective indicator
Externí odkaz:
http://arxiv.org/abs/1303.2236