Search Results for author:
Found 5 papers, 1 papers with code
Minimum discrepancy principle strategy for choosing $k$ in $k$-NN regression
We treat the problem of choosing the hyperparameter as an iterative procedure (over $k$) and propose using an easily implemented in practice strategy based on the idea of early stopping and the minimum discrepancy principle.
Early stopping and polynomial smoothing in regression with reproducing kernels
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.
Analyzing the discrepancy principle for kernelized spectral filter learning algorithms
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.
New efficient algorithms for multiple change-point detection with kernels
Finally, simulations confirmed the higher statistical accuracy of kernel-based approaches to detect changes that are not only in the mean.
Stability revisited: new generalisation bounds for the Leave-one-Out
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.
