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/397106/8067693
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
% targets
www.eeworm.com/read/247181/12675826
m emats.m
function [A,M] = Emats(mesh,withbd,scal)
% Computes "mass matrix" and (discretely regularized) "stiffness matrix"
% for edge elements
%
% mesh -> data structure for 2D triangulation
% withbd -> If tru
www.eeworm.com/read/316604/13520473
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/312163/13617487
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/359185/6352540
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/493206/6398550
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/410924/11264960
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/150760/12265921
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/131588/14136332
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/129915/14217738
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