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Analyzing the discrepancy principle for kernelized spectral filter learning algorithms
Alain Celisse, Martin Wahl
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 unk…
New efficient algorithms for multiple change-point detection with kernels
Alain Celisse, Guillemette Marot, Morgane Pierre-Jean +1
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 mea…
Theoretical analysis of cross-validation for estimating the risk of the k-Nearest Neighbor classifier
Alain Celisse, Tristan Mary-Huard
The present work aims at deriving theoretical guaranties on the behavior of some cross-validation procedures applied to the -nearest neighbors (NN) rule in the context of bin…
A One-Sample Test for Normality with Kernel Methods
Jérémie Kellner, Alain Celisse
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 distribu…
New normality test in high dimension with kernel methods
Jérémie Kellner, Alain Celisse
A new goodness-of-fit test for normality in high-dimension (and Reproducing Kernel Hilbert Space) is proposed. It shares common ideas with the Maximum Mean Discrepancy (MMD) it out…
A Kernel Multiple Change-point Algorithm via Model Selection
Sylvain Arlot, Alain Celisse, Zaid Harchaoui
We tackle the change-point problem with data belonging to a general set. We build a penalty for choosing the number of change-points in the kernel-based method of Harchaoui and Cap…