代码搜索：Classify

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m start_classify.m

function [D, test_err, train_err, train_patterns, train_targets, reduced_patterns, reduced_targets] = start_classify(patterns, targets, error_method, redraws, percent, Preprocessing_algorithm, Preproc
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www.eeworm.com/read/245941/12771161

m classify_paramteric.m

function targets = classify_paramteric(param_struct, patterns) %Function for classifying patterns based on parametric distributions. %Inputs are the paramters of the distributions and the patterns
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exe svm_classify.exe

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www.eeworm.com/read/143706/12849463

m test_classify.m

function run = test_classify(classifier) warning('off','MATLAB:colon:operandsNotRealScalar'); % clear global preprocess; global preprocess; global temp_train_file temp_test_file temp_output_f
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www.eeworm.com/read/143706/12850020

m gmm_classify.m

function [Y_compute, Y_prob] = GMM_classify(para, X_train, Y_train, X_test, Y_test, num_class) Y_compute = zeros(size(Y_test)); Y_prob = zeros(size(Y_test)); if (isempty(X_train)), fprintf('E
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www.eeworm.com/read/143706/12850111

m knn_classify.m

function [Y_compute, Y_prob] = kNN_classify(para, X_train, Y_train, X_test, Y_test, num_class) Y_compute = zeros(size(Y_test)); Y_prob = zeros(size(Y_test)); if (isempty(X_train)), fprintf('E
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www.eeworm.com/read/143706/12850133

m lda_classify.m

function [Y_compute, Y_prob] = LDA_classify(para, X_train, Y_train, X_test, Y_test, num_class) Y_compute = zeros(size(Y_test)); Y_prob = zeros(size(Y_test)); if (isempty(X_train)), error('T
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www.eeworm.com/read/143706/12850145

m iis_classify.m

function [Y_compute, Y_prob] = IIS_classify(para, X_train, Y_train, X_test, Y_test, num_class) % This function will employ IIS algorithm to find the optimal weight class_set = GetClassSet(Y_trai
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www.eeworm.com/read/143706/12850149

m gp_classify.m

function [Y_compute, Y_prob] = GP_classify(para, X_train, Y_train, X_test, Y_test, num_class) % Gaussian Process for Classification/Regression % Not Ready yet global temp_model_file; [class_set
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www.eeworm.com/read/143706/12850361

m svm_classify.m

function status = svm_classify(options, data, model, predictions) % SVM_CLASSIFY - Interface to SVM light, classification module % % STATUS = SVM_CLASSIFY(OPTIONS, DATA, MODEL, PREDICTIONS) % C
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