代码搜索：Classify
找到约 2,639 项符合「Classify」的源代码
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www.eeworm.com/read/405069/11472168
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/405069/11472184
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/405069/11472189
m perceptron_voted.m
function test_targets = Perceptron_Voted(train_patterns, train_targets, test_patterns, params)
% Classify using the Voted Perceptron algorithm
% Inputs:
% train_patterns - Train patterns
% trai
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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/405069/11472204
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
www.eeworm.com/read/405069/11472255
m locboost.m
function [test_targets, P, theta, phi] = LocBoost(train_patterns, train_targets, test_patterns, params)
% Classify using the local boosting algorithm
% Inputs:
% train_patterns - Train patterns
www.eeworm.com/read/405069/11472266
m lms.m
function [test_targets, a, updates] = LMS(train_patterns, train_targets, test_patterns, params)
% Classify using the least means square algorithm
% Inputs:
% train_patterns - Train patterns
% t
www.eeworm.com/read/405069/11472298
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/405069/11472315
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/405068/11472335
m backpropagation_stochastic.m
function [test_targets, test_hiddens, Wh, Wo, J] = Backpropagation_Stochastic(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with stochastic learni