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/286662/8751763
m kohonen_sofm.m
function [patterns, targets, label] = Kohonen_SOFM(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using a Kohonen self-organizing feature map algorithm
%Inputs:
www.eeworm.com/read/286662/8751768
m information_based_selection.m
function [patterns, targets, remaining_patterns] = Information_based_selection(patterns, targets, Npatterns)
% Koller and Sawami algorithm for pattern selection
%
% train_patterns - Input pattern
www.eeworm.com/read/372113/9521086
m nearestneighborediting.m
function [patterns, targets] = NearestNeighborEditing(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using the nearest neighbor editing algorithm
%Inputs:
% train_
www.eeworm.com/read/372113/9521129
m addc.m
function [patterns, targets] = ADDC(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using the Agglomerative clustering algorithm
%Inputs:
% train_patterns - Input p
www.eeworm.com/read/372113/9521146
m nddf.m
function [test_targets, g0, g1] = NDDF(train_patterns, train_targets, test_patterns, cost)
% Classify using the normal density discriminant function
% Inputs:
% train_patterns - Train patterns
www.eeworm.com/read/372113/9521155
m kohonen_sofm.m
function [patterns, targets, label] = Kohonen_SOFM(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using a Kohonen self-organizing feature map algorithm
%Inputs:
www.eeworm.com/read/372113/9521159
m information_based_selection.m
function [patterns, targets, remaining_patterns] = Information_based_selection(patterns, targets, Npatterns)
% Koller and Sawami algorithm for pattern selection
%
% train_patterns - Input pattern
www.eeworm.com/read/362008/10023775
m nearestneighborediting.m
function [patterns, targets] = NearestNeighborEditing(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using the nearest neighbor editing algorithm
%Inputs:
% train_
www.eeworm.com/read/362008/10023829
m addc.m
function [patterns, targets] = ADDC(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using the Agglomerative clustering algorithm
%Inputs:
% train_patterns - Input p
www.eeworm.com/read/362008/10023842
m nddf.m
function [test_targets, g0, g1] = NDDF(train_patterns, train_targets, test_patterns, cost)
% Classify using the normal density discriminant function
% Inputs:
% train_patterns - Train patterns