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/357874/10199213
m perceptron_vim.m
function [test_targets, a] = Perceptron_VIM(train_patterns, train_targets, test_patterns, params)
% Classify using the variable incerement Perceptron with margin algorithm
% Inputs:
% train_pat
www.eeworm.com/read/349842/10796619
m ls.m
function [D, w] = LS(train_features, train_targets, weights, region)
% Classify using the least-squares algorithm
% Inputs:
% features- Train features
% targets - Train targets
% Weights - Wei
www.eeworm.com/read/349842/10796665
m discrete_bayes.m
function D = Discrete_Bayes(train_features, train_targets, cost, region, test_feature)
% Classify discrete features using the Bayes decision theory
% Inputs:
% features - Train features
% targ
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m rbf_network.m
function [D, mu, Wo] = RBF_Network(train_features, train_targets, Nh, region)
% Classify using a backpropagation network with a batch learning algorithm
% Inputs:
% features- Train features
% t
www.eeworm.com/read/456028/7359592
txt table_id.txt
classify content
1 select
1 exit
1 ShowKeywords
1 table_key
1 select1
1 ShowID
1 table_ID
1 select1
1 Sho
www.eeworm.com/read/440433/7689450
m backpropagation_quickprop.m
function [test_targets, Wh, Wo, J] = Backpropagation_Quickprop(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with a batch learning algorithm and q
www.eeworm.com/read/399996/7816591
m backpropagation_batch.m
function [test_targets, Wh, Wo, J] = Backpropagation_Batch(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with a batch learning algorithm
% Inputs
www.eeworm.com/read/399996/7816609
m perceptron_bvi.m
function [test_targets, a] = Perceptron_BVI(train_patterns, train_targets, test_patterns, params)
% Classify using the batch variable increment Perceptron algorithm
% Inputs:
% train_patterns -
www.eeworm.com/read/399996/7816621
m bayesian_model_comparison.m
function test_targets = Bayesian_Model_Comparison(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the Bayesian model comparison algorithm. This function accepts as inputs
www.eeworm.com/read/399996/7816643
m backpropagation_quickprop.m
function [test_targets, Wh, Wo, J] = Backpropagation_Quickprop(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with a batch learning algorithm and q