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/245941/12771157
m rbf_network.m
function [test_targets, mu, Wo] = RBF_Network(train_patterns, train_targets, test_patterns, Nh)
% Classify using a backpropagation network with a batch learning algorithm
% Inputs:
% train_patte
www.eeworm.com/read/245941/12771213
asv parzen.asv
function test_targets = parzen(train_patterns, train_targets, test_patterns, hn)
% Classify using the Parzen windows algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets - Trai
www.eeworm.com/read/245941/12771216
m pocket.m
function [test_targets, w_pocket] = Pocket(train_patterns, train_targets, test_patterns, alg_param)
% Classify using the pocket algorithm (an improvement on the perceptron)
% Inputs:
% train_pat
www.eeworm.com/read/245941/12771224
m gibbs.m
function test_targets = Gibbs(train_patterns, train_targets, test_patterns, Ndiv)
% Classify using the Gibbs algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets - Train target
www.eeworm.com/read/245941/12771229
m stumps.m
function [test_targets, w] = Stumps(train_patterns, train_targets, test_patterns, params)
% Classify using simple stumps algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets -
www.eeworm.com/read/245941/12771239
asv em.asv
function [test_targets, param_struct] = EM(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the expectation-maximization algorithm
% Inputs:
% train_patterns - Train pa
www.eeworm.com/read/330850/12864700
m parzen.m
function test_targets = parzen(train_patterns, train_targets, test_patterns, hn)
% Classify using the Parzen windows algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets - Trai
www.eeworm.com/read/330850/12864744
m discrete_bayes.m
function test_targets = Discrete_Bayes(train_patterns, train_targets, test_patterns, cost)
% Classify discrete patterns using the Bayes decision theory
% Inputs:
% train_patterns - Train pattern
www.eeworm.com/read/330850/12864894
m projection_pursuit.m
function [test_targets, V, Wo] = Projection_Pursuit(train_patterns, train_targets, test_patterns, Ncomponents)
% Classify using projection pursuit regression
% Inputs:
% train_patterns - Train p
www.eeworm.com/read/330850/12865019
m balanced_winnow.m
function [test_targets, a_plus, a_minus] = Balanced_Winnow(train_patterns, train_targets, test_patterns, params)
% Classify using the balanced Winnow algorithm
% Inputs:
% training_patterns -