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/289321/8559302
m regem.m
function [X, M, C, Xerr] = regem(X, options)
%REGEM Imputation of missing values with regularized EM algorithm.
%
% [X, M, C, Xerr] = REGEM(X, OPTIONS) replaces missing values
% (NaNs) in the
www.eeworm.com/read/289321/8559307
m gcvridge.m
function h_opt = gcvridge(F, d, trS0, n, r, trSmin, options)
%GCVRIDGE Finds minimum of GCV function for ridge regression.
%
% GCVRIDGE(F, d, trS0, n, r, trSmin, OPTIONS) finds the
% regularizat
www.eeworm.com/read/386050/8768350
m ldc.m
%LDC Linear Bayes Normal Classifier (BayesNormal_1)
%
% [W.R,S,M] = LDC(A,R,S,M)
% W = A*LDC([],R,S,M);
%
% INPUT
% A Dataset
% R,S Regularization parameters, 0
www.eeworm.com/read/161189/10439711
m regudemo.m
%REGUDEMO Tutorial script for Regularization Tools.
% Per Christian Hansen, IMM, Feb. 21, 2001.
echo on, clf
% Part 1. The discrete Picard condition
% --------------------------------------
www.eeworm.com/read/418911/10891924
m regudemo.m
%REGUDEMO Tutorial script for Regularization Tools.
% Per Christian Hansen, IMM, Feb. 21, 2001.
echo on
clf
% Part 1. The discrete Picard condition
% --------------------------------------
%
% Fir
www.eeworm.com/read/299984/7140368
m ldc.m
%LDC Linear Bayes Normal Classifier (BayesNormal_1)
%
% [W.R,S,M] = LDC(A,R,S,M)
% W = A*LDC([],R,S,M);
%
% INPUT
% A Dataset
% R,S Regularization parameters, 0
www.eeworm.com/read/460435/7250843
m ldc.m
%LDC Linear Bayes Normal Classifier (BayesNormal_1)
%
% [W.R,S,M] = LDC(A,R,S,M)
% W = A*LDC([],R,S,M);
%
% INPUT
% A Dataset
% R,S Regularization parameters, 0
www.eeworm.com/read/450608/7480416
m ldc.m
%LDC Linear Bayes Normal Classifier (BayesNormal_1)
%
% W = LDC(A,R,S)
%
% INPUT
% A Dataset
% R,S Regularization parameters, 0
www.eeworm.com/read/441245/7673057
m ldc.m
%LDC Linear Bayes Normal Classifier (BayesNormal_1)
%
% [W.R,S,M] = LDC(A,R,S,M)
% W = A*LDC([],R,S,M);
%
% INPUT
% A Dataset
% R,S Regularization parameters, 0
www.eeworm.com/read/139775/13134957
m eigen_filtering.m
function [s,w,g] = eigen_filtering(y,p,mu);
% Given a 1D noisy sequence y, the order p of
% the ARMA(p,p) model and the regularization parameter mu
% this function computes the clean signal s, an es