代码搜索：Patterns
找到约 8,017 项符合「Patterns」的源代码
代码结果 8,017
www.eeworm.com/read/317622/13500814
m cascade_correlation.m
function [test_targets, Wh, Wo, J] = Cascade_Correlation(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with the cascade-correlation algorithm
% I
www.eeworm.com/read/317622/13500844
m svm.m
function [test_targets, a_star] = SVM(train_patterns, train_targets, test_patterns, params)
% Classify using (a very simple implementation of) the support vector machine algorithm
%
% Inputs:
%
www.eeworm.com/read/317622/13500853
m ada_boost.m
function [test_targets, E] = ada_boost(train_patterns, train_targets, test_patterns, params)
% Classify using the AdaBoost algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets
www.eeworm.com/read/405069/11472161
m cascade_correlation.m
function [test_targets, Wh, Wo, J] = Cascade_Correlation(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with the cascade-correlation algorithm
% I
www.eeworm.com/read/405069/11472192
m svm.m
function [test_targets, a_star] = SVM(train_patterns, train_targets, test_patterns, params)
% Classify using (a very simple implementation of) the support vector machine algorithm
%
% Inputs:
%
www.eeworm.com/read/405069/11472201
m ada_boost.m
function [test_targets, E] = ada_boost(train_patterns, train_targets, test_patterns, params)
% Classify using the AdaBoost algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets
www.eeworm.com/read/405068/11472334
m backpropagation_stochastic_multioutput.m
function [test_targets, tvh, Wh, Wo, J] = Backpropagation_Stochastic_MultiOutput(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with stochastic lea
www.eeworm.com/read/339628/12216116
m svm.m
function [test_targets, a_star] = SVM(train_patterns, train_targets, test_patterns, params)
% Classify using (a very simple implementation of) the support vector machine algorithm
%
% Inputs:
%
www.eeworm.com/read/123947/14605466
m prob1_trn.m
TRN_FLAG = 1;
%%%%%%%%%%%%%% Training file: prob1.trn %%%%%%%%%%%%
N_Patterns = 7; %Number of Patterns
%Weights_File_Name = 'prob1_wts.mat';
%####### Input Training Patterns ###########
www.eeworm.com/read/474600/6813409
m cascade_correlation.m
function [test_targets, Wh, Wo, J] = Cascade_Correlation(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with the cascade-correlation algorithm
% I