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/372113/9521132
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/372113/9521153
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/362008/10023779
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/362008/10023831
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/362008/10023848
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/357874/10199053
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/357874/10199083
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/357874/10199092
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/399996/7816595
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/399996/7816694
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:
%