Results 231 to 240 of about 58,998 (260)

Rek-Surv: A lightweight deep survival model for plant infectious disease onset prediction. [PDF]

Infect Dis Model
Xiao J, Hu S, Deng X, Lan Y, Xu M, Dai F, Li Y, You J, Zhang G, Liu Y.   +9 more
europepmc   +1 more source

Towards Interpretable Seizure Detection: An Excitation/Inhibition Dynamic Polynomial Network Framework for Electroencephalography. [PDF]

Sensors (Basel)
Sun X, Yan Y, Liu N, Fang S, Cai J, Wu EQ, Song A, Xu J.   +7 more
europepmc   +1 more source

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A Regularization Parameter for Nonsmooth Tikhonov Regularization

SIAM Journal of Scientific Computing, 2011
In this paper we develop a novel rule for choosing regularization parameters in nonsmooth Tikhonov functionals. It is solely based on the value function and applicable to a broad range of nonsmooth models, and it extends one known criterion. A posteriori error estimates of the approximations are derived.
Bangti Jin, Kazufumi Itô
exaly   +2 more sources

Regularity of Normal Forms on Parameters

Milan Journal of Mathematics, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Luis Barreira, Claudia Valls
openaire   +1 more source

Adaptive updating of regularization parameters

Signal Processing, 2015
Iterative minimization of an objective function is usually used for restoring a signal from its noisy measurements. The performance of such iterative algorithms is controlled by regularization parameters, such as Lagrange multipliers. Inappropriate choice of these parameters can either trap the algorithm in local minima and/or lead to a lower ...
SayedMasoud Hashemi, Soosan Beheshti, Richard S. C. Cobbold, Narinder S. Paul   +3 more
openaire   +1 more source

On the Selection of the Regularization Parameter in Stacking

Neural Processing Letters, 2020
Stacking is a model combination technique to improve prediction accuracy. Regularization is usually necessary in stacking because some predictions used in the model combination provide similar predictions. Cross-validation is generally used to select the regularization parameter, but it incurs a high computational cost.
openaire   +1 more source

Iterative evaluation of the regularization parameter in regularized image restoration

Journal of Visual Communication and Image Representation, 1992
In this paper a nonlinear regularized iterative image restoration algorithm is proposed, according to which no prior knowledge about the noise variance is assumed. The algorithm results from a set-theoretic regularization approach, where bounds of the stabilizing functional and the noise variance, which determine the regularization parameter, are ...
Aggelos K. Katsaggelos, Moon Gi Kang
openaire   +1 more source

Lasso screening with a small regularization parameter

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
Screening for lasso problems is a means of quickly reducing the size of the dictionary needed to solve a given instance without impacting the optimality of the solution obtained. We investigate a sequential screening scheme using a selected sequence of regularization parameter values decreasing to the given target value.
Yun Wang, Zhen James Xiang, Peter J. Ramadge   +2 more
openaire   +1 more source