代码搜索：Classify

找到约 2,639 项符合「Classify」的源代码

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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
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www.eeworm.com/read/357874/10199095

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
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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
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www.eeworm.com/read/357874/10199157

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
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www.eeworm.com/read/357874/10199189

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
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www.eeworm.com/read/357874/10199206

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 -
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www.eeworm.com/read/353439/10446390

java compareints.java

// control/CompareInts.java // TIJ4 Chapter Control, Exercise 2, page 139 /* Write a program that generates 25 random int values. For each value, use an * if-else statement to classify it as greate
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www.eeworm.com/read/352425/10553365

java compareints.java

// control/CompareInts.java // TIJ4 Chapter Control, Exercise 2, page 139 /* Write a program that generates 25 random int values. For each value, use an * if-else statement to classify it as greate
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www.eeworm.com/read/349842/10796916

m ho_kashyap.m

function [D, w_percept, b] = Ho_Kashyap(train_features, train_targets, params, region) % Classify using the using the Ho-Kashyap algorithm % Inputs: % features - Train features % targets -
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www.eeworm.com/read/349842/10796997

m ml_ii.m

function D = ML_II(train_features, train_targets, Ngaussians, region) % Classify using the ML-II algorithm. This function accepts as inputs the maximum number % of Gaussians per class and returns
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