代码搜索：Regularization
找到约 355 项符合「Regularization」的源代码
代码结果 355
www.eeworm.com/read/450939/7474357
m gradientregulization.m
% Computes the gradient of the regularization term of the super-resolution
% minimization function. This function implements the gradient of the
% bilateral-filter.
%
% Inputs:
% Xn - The current
www.eeworm.com/read/287267/8699032
m mregwav1.m
% Example of multiscale approximation using
% Regularization Networks
%
% Learning parameters "lambda" have to be tuned
% for instance by means of a cross-validation.
% Wavelet frame are used f
www.eeworm.com/read/152580/12100920
m mregwav1.m
% Example of multiscale approximation using
% Regularization Networks
%
% Learning parameters "lambda" have to be tuned
% for instance by means of a cross-validation.
% Wavelet frame are used f
www.eeworm.com/read/289488/8548352
m semipregex1.m
% comparing different settings of semiparametric
% regularization
%
% sin +gaussian
% wavelet
% wavelet + gaussian
% sinc + sin
%
% function to be approximated : sin(x) + sinc(x-5)+s
www.eeworm.com/read/287267/8699046
m framekernelex3.m
%
%
% Comparison of RKHS Kernel, Dual Green Kernel and
% Frame Green Kernel Performance with regularization
% parameter adjusted by a massive cross-validation.
%
% This script can also b
www.eeworm.com/read/287267/8699116
m semipregex1.m
% comparing different settings of semiparametric
% regularization
%
% sin +gaussian
% wavelet
% wavelet + gaussian
% sinc + sin
%
% function to be approximated : sin(x) + sinc(x-5)+s
www.eeworm.com/read/386050/8769048
m lassor.m
%LASSOR LASSO regression
%
% W = LASSOR(X,LAMBDA)
%
% INPUT
% X Regression dataset
% LAMBDA Regularization parameter
%
% OUTPUT
% W LASSO regression mapping
%
% DESCRIPTION
% Th
www.eeworm.com/read/299984/7140564
m lassor.m
%LASSOR LASSO regression
%
% W = LASSOR(X,LAMBDA)
%
% INPUT
% X Regression dataset
% LAMBDA Regularization parameter
%
% OUTPUT
% W LASSO regression mapping
%
% DESCRIPTION
% Th
www.eeworm.com/read/461039/7235541
m semipregex1.m
% comparing different settings of semiparametric
% regularization
%
% sin +gaussian
% wavelet
% wavelet + gaussian
% sinc + sin
%
% function to be approximated : sin(x) + sinc(x-5)+s
www.eeworm.com/read/460435/7251040
m lassor.m
%LASSOR LASSO regression
%
% W = LASSOR(X,LAMBDA)
%
% INPUT
% X Regression dataset
% LAMBDA Regularization parameter
%
% OUTPUT
% W LASSO regression mapping
%
% DESCRIPTION
% Th