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/286662/8751911
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/286662/8752018
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/372113/9521285
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/372113/9521383
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/362008/10023960
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/362008/10024032
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/357874/10199157
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/357874/10199204
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/399996/7816944
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/399996/7817091
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