Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Célisse, Alain"'
Autor:
Celisse, Alain
L'objet de cette thèse est l'étude d'un certain type d'algorithmes de rééchantillonnage regroupés sous le nom de validation-croisée, et plus particulièrement parmi eux, du leave-p-out. Très utilisés en pratique, ces algorithmes sont encore m
Externí odkaz:
http://tel.archives-ouvertes.fr/tel-00346320
http://tel.archives-ouvertes.fr/docs/00/34/63/20/PDF/PhDManuscript.pdf
http://tel.archives-ouvertes.fr/docs/00/34/63/20/PDF/PhDManuscript.pdf
The goal of anomaly detection is to identify observations that are generated by a distribution that differs from the reference distribution that qualifies normal behavior. When examining a time series, the reference distribution may evolve over time.
Externí odkaz:
http://arxiv.org/abs/2402.03565
The goal of anomaly detection is to identify observations generated by a process that is different from a reference one. An accurate anomaly detector must ensure low false positive and false negative rates. However in the online context such a constr
Externí odkaz:
http://arxiv.org/abs/2312.01969
Autor:
Averyanov, Yaroslav, Celisse, Alain
We present a novel data-driven strategy to choose the hyperparameter $k$ in the $k$-NN regression estimator without using any hold-out data. We treat the problem of choosing the hyperparameter as an iterative procedure (over $k$) and propose using an
Externí odkaz:
http://arxiv.org/abs/2008.08718
Autor:
Averyanov, Yaroslav, Celisse, Alain
In this paper, we study the problem of early stopping for iterative learning algorithms in a reproducing kernel Hilbert space (RKHS) in the nonparametric regression framework. In particular, we work with the gradient descent and (iterative) kernel ri
Externí odkaz:
http://arxiv.org/abs/2007.06827
Autor:
Celisse, Alain, Wahl, Martin
We investigate the construction of early stopping rules in the nonparametric regression problem where iterative learning algorithms are used and the optimal iteration number is unknown. More precisely, we study the discrepancy principle, as well as m
Externí odkaz:
http://arxiv.org/abs/2004.08436
Several statistical approaches based on reproducing kernels have been proposed to detect abrupt changes arising in the full distribution of the observations and not only in the mean or variance. Some of these approaches enjoy good statistical propert
Externí odkaz:
http://arxiv.org/abs/1710.04556
Autor:
Celisse, Alain, Guedj, Benjamin
The present paper provides a new generic strategy leading to non-asymptotic theoretical guarantees on the Leave-one-Out procedure applied to a broad class of learning algorithms. This strategy relies on two main ingredients: the new notion of $L^q$ s
Externí odkaz:
http://arxiv.org/abs/1608.06412
Autor:
Celisse, Alain, Mary-Huard, Tristan
The present work aims at deriving theoretical guaranties on the behavior of some cross-validation procedures applied to the $k$-nearest neighbors ($k$NN) rule in the context of binary classification. Here we focus on the leave-$p$-out cross-validatio
Externí odkaz:
http://arxiv.org/abs/1508.04905
Autor:
Kellner, Jérémie, Celisse, Alain
We propose a new one-sample test for normality in a Reproducing Kernel Hilbert Space (RKHS). Namely, we test the null-hypothesis of belonging to a given family of Gaussian distributions. Hence our procedure may be applied either to test data for norm
Externí odkaz:
http://arxiv.org/abs/1507.02904