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/405069/11472266
m lms.m
function [test_targets, a, updates] = LMS(train_patterns, train_targets, test_patterns, params)
% Classify using the least means square algorithm
% Inputs:
% train_patterns - Train patterns
% t
www.eeworm.com/read/405069/11472313
m pocket.m
function [test_targets, w_pocket] = Pocket(train_patterns, train_targets, test_patterns, alg_param)
% Classify using the pocket algorithm (an improvement on the perceptron)
% Inputs:
% train_pat
www.eeworm.com/read/474600/6813522
m lms.m
function [test_targets, updates] = LMS(train_patterns, train_targets, test_patterns, params)
% Classify using the least means square algorithm
% Inputs:
% train_patterns - Train patterns
% trai
www.eeworm.com/read/474600/6813575
m pocket.m
function [test_targets, w_pocket] = Pocket(train_patterns, train_targets, test_patterns, alg_param)
% Classify using the pocket algorithm (an improvement on the perceptron)
% Inputs:
% train_pat
www.eeworm.com/read/474600/6813577
m basebagging.m
function test_targets = BaseBagging(train_patterns, train_targets, test_patterns, params)
% Classify using the Bagging algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets - Tr
www.eeworm.com/read/286662/8751885
m leader_follower.m
function [patterns, targets, label, W] = Leader_Follower(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the basic leader-follower clustering algorithm
%Inp
www.eeworm.com/read/286662/8751958
m deterministic_sa.m
function [patterns, targets] = Deterministic_SA(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the deterministic simulated annealing algorithm
%Inputs:
%
www.eeworm.com/read/372113/9521264
m leader_follower.m
function [patterns, targets, label, W] = Leader_Follower(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the basic leader-follower clustering algorithm
%Inp
www.eeworm.com/read/372113/9521337
m deterministic_sa.m
function [patterns, targets] = Deterministic_SA(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the deterministic simulated annealing algorithm
%Inputs:
%
www.eeworm.com/read/362008/10023941
m leader_follower.m
function [patterns, targets, label, W] = Leader_Follower(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the basic leader-follower clustering algorithm
%Inp