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/397099/8068942
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/245941/12770740
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/245941/12770798
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/245941/12770989
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/245941/12771021
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/330850/12864707
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/330850/12864796
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/330850/12864999
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/330850/12865019
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/317622/13500811
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:
%