代码搜索：Regularized
找到约 102 项符合「Regularized」的源代码
代码结果 102
www.eeworm.com/read/191566/8428193
m~ tps_iter_match_1.m~
% script for doing shape-context based matching with alternating steps
% of estimating correspondences and estimating the regularized TPS
% transformation
% initialize transformed version of mode
www.eeworm.com/read/191566/8428204
m tps_iter_match_1.m
% script for doing shape-context based matching with alternating steps
% of estimating correspondences and estimating the regularized TPS
% transformation
% initialize transformed version of mode
www.eeworm.com/read/356592/10224211
m~ tps_iter_match_1.m~
% script for doing shape-context based matching with alternating steps
% of estimating correspondences and estimating the regularized TPS
% transformation
% initialize transformed version of mode
www.eeworm.com/read/356592/10224215
m tps_iter_match_1.m
% script for doing shape-context based matching with alternating steps
% of estimating correspondences and estimating the regularized TPS
% transformation
% initialize transformed version of mode
www.eeworm.com/read/440302/1799000
m zeroprecmatrixonepsf.m
function precMatData = zeroPrecOnePsf(PSF, center, b, tol)
%
% precMatData = zeroPrecOnePsf(PSF, center, b, tol);
%
% Construct the data needed for a regularized circulant preconditioner
%
% In
www.eeworm.com/read/191902/8417249
m rda.m
function D = RDA (train_features, train_targets, lamda, region)
% Classify using the Regularized descriminant analysis (Friedman shrinkage algorithm)
% Inputs:
% features - Train features
% tar
www.eeworm.com/read/428849/8834710
m greedykls.m
function [model,Z]=greedykpca(X,y,options)
% GREEDYKLS Greedy Regularized Kernel Least Squares.
%
% Synopsis:
% model = greedykls(X)
% model = greedykls(X,options)
%
% Description:
% This function
www.eeworm.com/read/177129/9468908
m rda.m
function D = RDA (train_features, train_targets, lamda, region)
% Classify using the Regularized descriminant analysis (Friedman shrinkage algorithm)
% Inputs:
% features - Train features
% tar
www.eeworm.com/read/362246/10010205
m greedykls.m
function [model,Z]=greedykpca(X,y,options)
% GREEDYKLS Greedy Regularized Kernel Least Squares.
%
% Synopsis:
% model = greedykls(X)
% model = greedykls(X,options)
%
% Description:
% This function
www.eeworm.com/read/349842/10796836
m rda.m
function D = RDA (train_features, train_targets, lamda, region)
% Classify using the Regularized descriminant analysis (Friedman shrinkage algorithm)
% Inputs:
% features - Train features
% tar