代码搜索：Classify
找到约 2,639 项符合「Classify」的源代码
代码结果 2,639
www.eeworm.com/read/399996/7816944
m lms.m
function [test_targets, updates] = LMS(train_patterns, train_targets, test_patterns, params)
% Classify using the least means square algorithm
% Inputs:
% train_patterns - Train patterns
% trai
www.eeworm.com/read/399996/7817029
m genetic_algorithm.m
function test_targets = Genetic_Algorithm(train_patterns, train_targets, test_patterns, params)
% Classify using a basic genetic algorithm
% Inputs:
% training_patterns - Train patterns
% tra
www.eeworm.com/read/399996/7817088
m bagging.m
function [test_targets] = Bagging(train_patterns, train_targets, test_patterns, params)
% Classify using the Bagging algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets - Trai
www.eeworm.com/read/399996/7817099
m basebagging.m
function test_targets = BaseBagging(train_patterns, train_targets, test_patterns, params)
% Classify using the Bagging algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets - Tr
www.eeworm.com/read/399996/7817102
m relaxation_ssm.m
function [test_targets, a] = Relaxation_SSM(train_patterns, train_targets, test_patterns, params)
% Classify using the single-sample relaxation with margin algorithm
% Inputs:
% train_patterns -
www.eeworm.com/read/397099/8068737
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
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m multivariate_splines.m
function test_targets = Multivariate_Splines(train_patterns, train_targets, test_patterns, params)
% Classify using multivariate adaptive regression splines
% Inputs:
% train_patterns - Train pa
www.eeworm.com/read/397099/8068779
m perceptron_batch.m
function [test_targets, a, updates] = Perceptron_Batch(train_patterns, train_targets, test_patterns, params)
% Classify using the batch Perceptron algorithm
% Inputs:
% train_patterns - Train pa
www.eeworm.com/read/397099/8068799
m optimal_brain_surgeon.m
function [test_targets, Wh, Wo, J] = Optimal_Brain_Surgeon(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with a batch learning algorithm and remov
www.eeworm.com/read/397099/8068814
m relaxation_bm.m
function [test_targets, a] = Relaxation_BM(train_patterns, train_targets, test_patterns, params)
% Classify using the batch relaxation with margin algorithm
% Inputs:
% train_patterns - Train pa