代码搜索：patterns

找到约 8,017 项符合「patterns」的源代码

代码结果 8,017
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www.eeworm.com/read/245941/12770742

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_
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www.eeworm.com/read/245941/12770814

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
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www.eeworm.com/read/245941/12770838

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
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www.eeworm.com/read/245941/12770852

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:
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www.eeworm.com/read/245941/12770869

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
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www.eeworm.com/read/245941/12771232

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
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www.eeworm.com/read/330850/12864710

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_
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www.eeworm.com/read/330850/12864816

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
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www.eeworm.com/read/330850/12864848

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
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www.eeworm.com/read/330850/12864861

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:
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