代码搜索：classification

找到约 3,679 项符合「classification」的源代码

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m svcinfo.m

function svcinfo(trn,tst,ker,alpha,bias) %SVCINFO Support Vector Classification Results % % Usage: svcinfo(trn,tst,ker,alpha,bias) % % Parameters: trn - Training set % tst -
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www.eeworm.com/read/376519/9315888

m decision_tree_cart.m

function D = CART(train_features, train_targets, params, region) % Classify using classification and regression trees % Inputs: % features - Train features % targets - Train targets % para
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www.eeworm.com/read/376519/9315892

m cart.m

function D = CART(train_features, train_targets, params, region) % Classify using classification and regression trees % Inputs: % features - Train features % targets - Train targets % para
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www.eeworm.com/read/177674/9442635

m demglm1.m

%DEMGLM1 Demonstrate simple classification using a generalized linear model. % % Description % The problem consists of a two dimensional input matrix DATA and a % vector of classifications T. The da
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www.eeworm.com/read/177674/9442697

m demglm2.m

%DEMGLM2 Demonstrate simple classification using a generalized linear model. % % Description % The problem consists of a two dimensional input matrix DATA and a % vector of classifications T. The da
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www.eeworm.com/read/177129/9468949

m cart.m

function D = CART(train_features, train_targets, params, region) % Classify using classification and regression trees % Inputs: % features - Train features % targets - Train targets % para
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www.eeworm.com/read/176823/9483324

m demglm1.m

%DEMGLM1 Demonstrate simple classification using a generalized linear model. % % Description % The problem consists of a two dimensional input matrix DATA and a % vector of classifications T. The da
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www.eeworm.com/read/176823/9483379

m demglm2.m

%DEMGLM2 Demonstrate simple classification using a generalized linear model. % % Description % The problem consists of a two dimensional input matrix DATA and a % vector of classifications T. The da
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www.eeworm.com/read/361769/10036403

m cart.m

function D = CART(train_features, train_targets, params, region) % Classify using classification and regression trees % Inputs: % features - Train features % targets - Train targets % param
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www.eeworm.com/read/360710/10080941

m knn.m

function [eachClass, ensembleClass, nearestSampleIndex, knnmat] = ... knn(sampledata, testdata, k) % KNN K-nearest neighbor rule for classification % Usage: % [EACH_CLASS, ENSEMBLE_CLASS, NEAREST
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