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/399996/7816868
m rda.m
function test_targets = RDA (train_patterns, train_targets, test_patterns, lamda)
% Classify using the Regularized descriminant analysis (Friedman shrinkage algorithm)
% Inputs:
% train_patterns
www.eeworm.com/read/397099/8068901
m rda.m
function test_targets = RDA (train_patterns, train_targets, test_patterns, lamda)
% Classify using the Regularized descriminant analysis (Friedman shrinkage algorithm)
% Inputs:
% train_patterns
www.eeworm.com/read/245941/12770976
m rda.m
function test_targets = RDA (train_patterns, train_targets, test_patterns, lamda)
% Classify using the Regularized descriminant analysis (Friedman shrinkage algorithm)
% Inputs:
% train_patterns
www.eeworm.com/read/330850/12864993
m rda.m
function test_targets = RDA (train_patterns, train_targets, test_patterns, lamda)
% Classify using the Regularized descriminant analysis (Friedman shrinkage algorithm)
% Inputs:
% train_patterns
www.eeworm.com/read/317622/13500896
m rda.m
function test_targets = RDA (train_patterns, train_targets, test_patterns, lamda)
% Classify using the Regularized descriminant analysis (Friedman shrinkage algorithm)
% Inputs:
% train_patterns
www.eeworm.com/read/405069/11472244
m rda.m
function test_targets = RDA (train_patterns, train_targets, test_patterns, lamda)
% Classify using the Regularized descriminant analysis (Friedman shrinkage algorithm)
% Inputs:
% train_patterns
www.eeworm.com/read/386597/2570173
m rda.m
function test_targets = RDA (train_patterns, train_targets, test_patterns, lamda)
% Classify using the Regularized descriminant analysis (Friedman shrinkage algorithm)
% Inputs:
% train_patterns
www.eeworm.com/read/474600/6813496
m rda.m
function test_targets = RDA (train_patterns, train_targets, test_patterns, lamda)
% Classify using the Regularized descriminant analysis (Friedman shrinkage algorithm)
% Inputs:
% train_patterns
www.eeworm.com/read/471135/6898144
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/268135/11150986
m rda.m
function test_targets = RDA (train_patterns, train_targets, test_patterns, lamda)
% Classify using the Regularized descriminant analysis (Friedman shrinkage algorithm)
% Inputs:
% train_patterns