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/362008/10023846
m stochastic_sa.m
function [patterns, targets] = Stochastic_SA(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the stochastic simulated annealing algorithm
%Inputs:
% train_
www.eeworm.com/read/362008/10024001
m sohc.m
function [patterns, targets, label] = SOHC(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using the stepwise optimal hierarchical clustering algorithm
%Inputs:
% t
www.eeworm.com/read/362008/10024020
m lvq3.m
function [patterns, targets] = LVQ3(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using linear vector quantization
%Inputs:
% train_patterns - Input patterns
% t
www.eeworm.com/read/362008/10024033
m components_with_df.m
function [test_targets, errors] = Components_with_DF(train_patterns, train_targets, test_patterns, Ncomponents)
% Classify points using component classifiers with discriminant functions
% Inputs:
www.eeworm.com/read/357874/10199062
m bayesian_model_comparison.m
function test_targets = Bayesian_Model_Comparison(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the Bayesian model comparison algorithm. This function accepts as inputs
www.eeworm.com/read/357874/10199091
m stochastic_sa.m
function [patterns, targets] = Stochastic_SA(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the stochastic simulated annealing algorithm
%Inputs:
% train_
www.eeworm.com/read/357874/10199183
m sohc.m
function [patterns, targets, label] = SOHC(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using the stepwise optimal hierarchical clustering algorithm
%Inputs:
% t
www.eeworm.com/read/357874/10199195
m lvq3.m
function [patterns, targets] = LVQ3(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using linear vector quantization
%Inputs:
% train_patterns - Input patterns
% t
www.eeworm.com/read/357874/10199205
m components_with_df.m
function [test_targets, errors] = Components_with_DF(train_patterns, train_targets, test_patterns, Ncomponents)
% Classify points using component classifiers with discriminant functions
% Inputs:
www.eeworm.com/read/399996/7816621
m bayesian_model_comparison.m
function test_targets = Bayesian_Model_Comparison(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the Bayesian model comparison algorithm. This function accepts as inputs