代码搜索：Classify

找到约 2,639 项符合「Classify」的源代码

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/App.inc/// D/BaseVCL/// D/Classify/// D/Common/// D/Components/// D/Customers/// D/DataAnalyse/// D/DepartInfo/// D/DepotBerths/// D/Employees/// D/FmMainEx/// D/GoodsBase/// D/GoodsPrice
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www.eeworm.com/read/191902/8417038

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/191902/8417079

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/191902/8417385

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/286662/8751646

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/286662/8751670

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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www.eeworm.com/read/286662/8751680

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/286662/8751701

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/286662/8751703

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/286662/8751731

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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