代码搜索：Classify

找到约 2,639 项符合「Classify」的源代码

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m nddf.m

function [D, g0, g1] = NDDF(train_features, train_targets, cost, region, test_feature) % Classify using the normal density discriminant function % Inputs: % features - Train features % target
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www.eeworm.com/read/213240/15140076

m dd_label.m

function z = dd_label(x,w,realoutput) %DD_LABEL classify the dataset and put labels in the dataset % % Z = DD_LABEL(X,W) % % Compute the output labels of objects X by mapping through mapping W % and
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www.eeworm.com/read/175689/5343325

m detreeexp31.m

global data textdata numobs bad load ant %% 作散点图 gscatter(data(:,1),data(:,2),textdata(:,1),'rgb','osv'); xlabel('Head-Width(毫米)'); ylabel('Weight(毫克)'); linclass = classify(data(:,1:2),
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www.eeworm.com/read/279486/4136298

m kmeans_class.m

function C = kmeans_class(M,trajs) %KMEANS_CLASS Classify test data % C = KMEANS_CLASS(Model,trajs) % Scott J. Gaffney 10 November 2004 % Department of Information and Computer Science % Univ
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www.eeworm.com/read/428780/1953999

m detreeexp31.m

global data textdata numobs bad load ant %% 作散点图 gscatter(data(:,1),data(:,2),textdata(:,1),'rgb','osv'); xlabel('Head-Width(毫米)'); ylabel('Weight(毫克)'); linclass = classify(data(:,1:2),
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www.eeworm.com/read/386597/2570086

m ls.m

function [test_targets, w] = LS(train_patterns, train_targets, test_patterns, weights) % Classify using the least-squares algorithm % Inputs: % train_patterns - Train patterns % train_targets
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www.eeworm.com/read/386597/2570097

m nearest_neighbor.m

function test_targets = Nearest_Neighbor(train_patterns, train_targets, test_patterns, Knn) % Classify using the Nearest neighbor algorithm % Inputs: % train_patterns - Train patterns % train_t
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www.eeworm.com/read/386597/2570133

m ada_boost.m

function [test_targets, E] = ada_boost(train_patterns, train_targets, test_patterns, params) % Classify using the AdaBoost algorithm % Inputs: % train_patterns - Train patterns % train_targets
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www.eeworm.com/read/386597/2570177

m local_polynomial.m

function test_targets = Local_Polynomial(train_patterns, train_targets, test_patterns, Nlp) % Classify using the local polynomial fitting % Inputs: % train_patterns - Train patterns % train_tar
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www.eeworm.com/read/386597/2570229

m ml_ii.m

function test_targets = ML_II(train_patterns, train_targets, test_patterns, Ngaussians) % Classify using the ML-II algorithm. This function accepts as inputs the maximum number % of Gaussians per
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