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/372113/9521117
m perceptron_batch.m
function [test_targets, a, updates] = Perceptron_Batch(train_patterns, train_targets, test_patterns, params)
% Classify using the batch Perceptron algorithm
% Inputs:
% train_patterns - Train pa
www.eeworm.com/read/372113/9521251
m ml.m
function test_targets = ML(train_patterns, train_targets, test_patterns, AlgorithmParameters)
% Classify using the maximum-likelyhood algorithm
% Inputs:
% train_patterns - Train patterns
% tra
www.eeworm.com/read/372113/9521270
m balanced_winnow.m
function [test_targets, a_plus, a_minus] = Balanced_Winnow(train_patterns, train_targets, test_patterns, params)
% Classify using the balanced Winnow algorithm
% Inputs:
% training_patterns -
www.eeworm.com/read/362008/10023774
m ml_diag.m
function test_targets = ML_diag(train_patterns, train_targets, test_patterns, AlgorithmParameters)
% Classify using the maximum likelyhood algorithm with diagonal covariance matrices
% Inputs:
%
www.eeworm.com/read/362008/10023787
m perceptron_bvi.m
function [test_targets, a] = Perceptron_BVI(train_patterns, train_targets, test_patterns, params)
% Classify using the batch variable increment Perceptron algorithm
% Inputs:
% train_patterns -
www.eeworm.com/read/362008/10023818
m perceptron_batch.m
function [test_targets, a, updates] = Perceptron_Batch(train_patterns, train_targets, test_patterns, params)
% Classify using the batch Perceptron algorithm
% Inputs:
% train_patterns - Train pa
www.eeworm.com/read/362008/10023928
m ml.m
function test_targets = ML(train_patterns, train_targets, test_patterns, AlgorithmParameters)
% Classify using the maximum-likelyhood algorithm
% Inputs:
% train_patterns - Train patterns
% tra
www.eeworm.com/read/362008/10023944
m balanced_winnow.m
function [test_targets, a_plus, a_minus] = Balanced_Winnow(train_patterns, train_targets, test_patterns, params)
% Classify using the balanced Winnow algorithm
% Inputs:
% training_patterns -
www.eeworm.com/read/362008/10024045
m perceptron_vim.m
function [test_targets, a] = Perceptron_VIM(train_patterns, train_targets, test_patterns, params)
% Classify using the variable incerement Perceptron with margin algorithm
% Inputs:
% train_pat
www.eeworm.com/read/357874/10199050
m ml_diag.m
function test_targets = ML_diag(train_patterns, train_targets, test_patterns, AlgorithmParameters)
% Classify using the maximum likelyhood algorithm with diagonal covariance matrices
% Inputs:
%