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/372113/9521103

m bayesian_model_comparison.m

function test_targets = Bayesian_Model_Comparison(train_patterns, train_targets, test_patterns, Ngaussians) % Classify using the Bayesian model comparison algorithm. This function accepts as inputs
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www.eeworm.com/read/372113/9521110

m backpropagation_quickprop.m

function [test_targets, Wh, Wo, J] = Backpropagation_Quickprop(train_patterns, train_targets, test_patterns, params) % Classify using a backpropagation network with a batch learning algorithm and q
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www.eeworm.com/read/372113/9521111

m minimum_cost.m

function test_targets = Minimum_Cost(train_patterns, train_targets, test_patterns, lambda) % Classify using the minimum error criterion via histogram estimation of the densities % Inputs: % trai
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www.eeworm.com/read/372113/9521128

m interactive_learning.m

function test_targets = Interactive_Learning(train_patterns, train_targets, test_patterns, params) % Classify using nearest neighbors and interactive learning % Inputs: % train_patterns - Train
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www.eeworm.com/read/372113/9521144

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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www.eeworm.com/read/372113/9521162

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/372113/9521286

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/372113/9521323

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/372113/9521402

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/362008/10023777

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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