代码搜索：Classify

找到约 2,639 项符合「Classify」的源代码

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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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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
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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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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/333003/12711472

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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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/245941/12770749

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
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www.eeworm.com/read/245941/12770798

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
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www.eeworm.com/read/245941/12770828

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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