代码搜索：Patterns
找到约 8,017 项符合「Patterns」的源代码
代码结果 8,017
www.eeworm.com/read/372113/9521174
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/372113/9521356
m rbf_network.m
function [test_targets, mu, Wo] = RBF_Network(train_patterns, train_targets, test_patterns, Nh)
% Classify using a radial basis function network algorithm
% Inputs:
% train_patterns - Train patt
www.eeworm.com/read/362008/10023763
m whitening_transform.m
function [new_patterns, train_targets, Aw, means] = Whitening_transform(train_patterns, train_targets, param, plot_on)
%Reshape the data points using the whitening transform
%Inputs:
% train_patt
www.eeworm.com/read/362008/10023770
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/362008/10023864
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/362008/10024014
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/357874/10199048
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/357874/10199102
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/357874/10199192
m rbf_network.m
function [test_targets, mu, Wo] = RBF_Network(train_patterns, train_targets, test_patterns, Nh)
% Classify using a radial basis function network algorithm
% Inputs:
% train_patterns - Train patt
www.eeworm.com/read/468976/6982725
m kernel_k_means.m
function [patterns, targets, label] = kernel_k_means(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the kernel k-means algorithm
%Inputs:
% train_patterns