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/405069/11472152
m whitening_transform.m
function [new_patterns, train_targets, Aw, means] = Whitening_transform(train_patterns, train_targets, param, plot_on)
%Reshape the data points using the whitening transform
%Inputs:
% train_patt
www.eeworm.com/read/405069/11472157
m scaling_transform.m
function [new_patterns, train_targets, var_mat, means] = Scaling_transform(train_patterns, train_targets, param, plot_on)
%Reshape the data points using the scaling transform
%Inputs:
% train_pat
www.eeworm.com/read/474600/6813400
m whitening_transform.m
function [new_patterns, train_targets, Aw, means] = Whitening_transform(train_patterns, train_targets, param, plot_on)
%Reshape the data points using the whitening transform
%Inputs:
% train_patt
www.eeworm.com/read/474600/6813405
m scaling_transform.m
function [new_patterns, train_targets, var_mat, means] = Scaling_transform(train_patterns, train_targets, param, plot_on)
%Reshape the data points using the scaling transform
%Inputs:
% train_pat
www.eeworm.com/read/349226/10840361
doc readme.doc
YOU NEED A LIFE PROGRAM TO VIEW THESE PATTERNS!
Here is a list of life programs that support these patterns:
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LIFE 1.06
www.eeworm.com/read/286662/8751637
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/286662/8751713
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/286662/8751874
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/286662/8751888
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/372113/9521084
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:
%