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/286662/8751864

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/286662/8751868

m components_without_df.m

function [test_targets, errors] = Components_without_DF(train_patterns, train_targets, test_patterns, Classifiers) % Classify points using component classifiers without discriminant functions % In
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www.eeworm.com/read/286662/8751878

m backpropagation_stochastic.m

function [test_targets, Wh, Wo, J] = Backpropagation_Stochastic(train_patterns, train_targets, test_patterns, params) % Classify using a backpropagation network with stochastic learning algorithm
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www.eeworm.com/read/286662/8751919

m cart.m

function test_targets = CART(train_patterns, train_targets, test_patterns, params) % Classify using classification and regression trees % Inputs: % training_patterns - Train patterns % traini
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www.eeworm.com/read/286662/8751969

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/285044/8874436

txt svm.txt

function [D, a_star] = SVM(train_features, train_targets, params, region) % Classify using (a very simple implementation of) the support vector machine algorithm % % Inputs: % features- Train f
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www.eeworm.com/read/177129/9468782

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/177129/9468816

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/177129/9468840

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/177129/9468848

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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