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/405069/11472177
m aghc.m
function [patterns, targets] = AGHC(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the agglomerative hierarchical clustering algorithm
%Inputs:
% train_pa
www.eeworm.com/read/405069/11472287
m fuzzy_k_means.m
function [patterns, targets] = fuzzy_k_means(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using the fuzzy k-means algorithm
%Inputs:
% train_patterns - Input pat
www.eeworm.com/read/405069/11472289
m k_means.m
function [patterns, targets, label] = k_means(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using the k-means algorithm
%Inputs:
% train_patterns - Input patterns
www.eeworm.com/read/405069/11472312
m competitive_learning.m
function [patterns, targets, label, W] = Competitive_learning(train_patterns, train_targets, params, plot_on)
% Perform preprocessing using a competitive learning network
% Inputs:
% patterns -
www.eeworm.com/read/474600/6813424
m aghc.m
function [patterns, targets] = AGHC(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the agglomerative hierarchical clustering algorithm
%Inputs:
% train_pa
www.eeworm.com/read/474600/6813502
asv k_means.asv
function [patterns, targets, label] = k_means(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using the k-means algorithm
%Inputs:
% train_patterns - Input patterns
www.eeworm.com/read/474600/6813521
asv aghc.asv
function [patterns, targets] = AGHC(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the agglomerative hierarchical clustering algorithm
%Inputs:
% train_pa
www.eeworm.com/read/474600/6813544
m fuzzy_k_means.m
function [patterns, targets] = fuzzy_k_means(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using the fuzzy k-means algorithm
%Inputs:
% train_patterns - Input pat
www.eeworm.com/read/474600/6813546
m k_means.m
function [patterns, targets, label] = k_means(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using the k-means algorithm
%Inputs:
% train_patterns - Input patterns
www.eeworm.com/read/474600/6813572
m competitive_learning.m
function [patterns, targets, label, W] = Competitive_learning(train_patterns, train_targets, params, plot_on)
% Perform preprocessing using a competitive learning network
% Inputs:
% patterns -