代码搜索：Classify

找到约 2,639 项符合「Classify」的源代码

代码结果 2,639
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www.eeworm.com/read/474600/6813554

m backpropagation_recurrent.m

function [test_targets, W, J] = Backpropagation_Recurrent(train_patterns, train_targets, test_patterns, params) % Classify using a backpropagation recurrent network with a batch learning algorithm
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www.eeworm.com/read/415311/11077044

m minimum_cost.m

function D = Minimum_Cost(train_features, train_targets, lambda, region) % Classify using the minimum error criterion via histogram estimation of the densities % Inputs: % features- Train featur
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www.eeworm.com/read/415311/11077081

m optimal_brain_surgeon.m

function [D, Wh, Wo] = Optimal_Brain_Surgeon(train_features, train_targets, params, region) % Classify using a backpropagation network with a batch learning algorithm and remove excess units % usi
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www.eeworm.com/read/415311/11077096

m perceptron.m

function D = Perceptron(train_features, train_targets, alg_param, region) % Classify using the Perceptron algorithm (Fixed increment single-sample perceptron) % Inputs: % features - Train featur
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www.eeworm.com/read/415311/11077101

m projection_pursuit.m

function [D, V, Wo] = Projection_Pursuit(train_features, train_targets, Ncomponents, region) % Classify using projection pursuit regression % Inputs: % features- Train features % targets - Trai
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www.eeworm.com/read/415311/11077179

m balanced_winnow.m

function [D, a_plus, a_minus] = Balanced_Winnow(train_features, train_targets, params, region) % Classify using the balanced Winnow algorithm % Inputs: % features - Train features % targets
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www.eeworm.com/read/415311/11077287

m rce.m

function D = RCE(train_features, train_targets, lambda_m, region) % Classify using the reduced coulomb energy algorithm % Inputs: % features - Train features % targets - Train targets % la
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www.eeworm.com/read/415311/11077338

m stumps.m

function [D, w] = Stumps(train_features, train_targets, params, region) % Classify using the least-squares algorithm % Inputs: % features- Train features % targets - Train targets % weights -
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www.eeworm.com/read/268135/11150986

m rda.m

function test_targets = RDA (train_patterns, train_targets, test_patterns, lamda) % Classify using the Regularized descriminant analysis (Friedman shrinkage algorithm) % Inputs: % train_patterns
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www.eeworm.com/read/104144/15704296

extra entries.extra

/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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