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/399996/7816716
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/399996/7816734
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-orgenizing pattern map algorithm
%Inputs:
www.eeworm.com/read/399996/7816752
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/399996/7817116
m ml_ii.m
function test_targets = ML_II(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the ML-II algorithm. This function accepts as inputs the maximum number
% of Gaussians per
www.eeworm.com/read/397099/8068735
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/397099/8068793
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/397099/8068804
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/397099/8068810
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-orgenizing pattern map algorithm
%Inputs:
www.eeworm.com/read/397099/8068816
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/397099/8069086
m ml_ii.m
function test_targets = ML_II(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the ML-II algorithm. This function accepts as inputs the maximum number
% of Gaussians per