代码搜索：Classify

找到约 2,639 项符合「Classify」的源代码

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m perceptron_fm.m

function [test_targets, a] = Perceptron_FM(train_patterns, train_targets, test_patterns, params) % Classify using the Perceptron algorithm but at each iteration updating the worst-classified sample
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m perceptron.m

function [test_targets, a] = Perceptron(train_patterns, train_targets, test_patterns, alg_param) % Classify using the Perceptron algorithm (Fixed increment single-sample perceptron) % Inputs: %
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www.eeworm.com/read/286662/8751914

m backpropagation_cgd.m

function [test_targets, Wh, Wo, errors] = Backpropagation_CGD(train_patterns, train_targets, test_patterns, params) % Classify using a backpropagation network with a batch learning algorithm and co
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www.eeworm.com/read/286662/8751947

m backpropagation_sm.m

function [test_targets, Wh, Wo, J] = Backpropagation_SM(train_patterns, train_targets, test_patterns, params) % Classify using a backpropagation network with stochastic learning algorithm with mome
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www.eeworm.com/read/286662/8752052

m perceptron_vim.m

function [test_targets, a] = Perceptron_VIM(train_patterns, train_targets, test_patterns, params) % Classify using the variable incerement Perceptron with margin algorithm % Inputs: % train_pat
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www.eeworm.com/read/177129/9468735

m ls.m

function [D, w] = LS(train_features, train_targets, weights, region) % Classify using the least-squares algorithm % Inputs: % features- Train features % targets - Train targets % Weights - Wei
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www.eeworm.com/read/177129/9468767

m discrete_bayes.m

function D = Discrete_Bayes(train_features, train_targets, cost, region, test_feature) % Classify discrete features using the Bayes decision theory % Inputs: % features - Train features % targ
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www.eeworm.com/read/177129/9469009

m rbf_network.m

function [D, mu, Wo] = RBF_Network(train_features, train_targets, Nh, region) % Classify using a backpropagation network with a batch learning algorithm % Inputs: % features- Train features % t
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www.eeworm.com/read/372113/9521088

m backpropagation_batch.m

function [test_targets, Wh, Wo, J] = Backpropagation_Batch(train_patterns, train_targets, test_patterns, params) % Classify using a backpropagation network with a batch learning algorithm % Inputs
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www.eeworm.com/read/372113/9521098

m perceptron_bvi.m

function [test_targets, a] = Perceptron_BVI(train_patterns, train_targets, test_patterns, params) % Classify using the batch variable increment Perceptron algorithm % Inputs: % train_patterns -
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