代码搜索：Classify
找到约 2,639 项符合「Classify」的源代码
代码结果 2,639
www.eeworm.com/read/359185/6352469
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/359185/6352489
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
www.eeworm.com/read/359185/6352584
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/493206/6398447
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/493206/6398467
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
www.eeworm.com/read/493206/6398594
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/410924/11264781
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
www.eeworm.com/read/410924/11265051
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/405069/11472160
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/405069/11472167
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 -