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/245941/12770811
m perceptron_voted.m
function test_targets = Perceptron_Voted(train_patterns, train_targets, test_patterns, params)
% Classify using the Perceptron algorithm
% Inputs:
% train_patterns - Train patterns
% train_targ
www.eeworm.com/read/245941/12770979
m components_without_df.m
function [test_targets, errors] = Components_without_DF(train_patterns, train_targets, test_patterns, Classifiers)
% Classify points using component classifiers without discriminant functions
% In
www.eeworm.com/read/245941/12771008
m trocchiobag.m
function [test_targets]=TRocchiobag(train_patterns,train_targets,test_patterns)
% a is always be 16
% b is always be 4
% [prownum,pcolumn]=size(allposx);% prownum is the number of postive rows
% [
www.eeworm.com/read/245941/12771034
m fisherslineardiscriminant.m
function [patterns, train_targets, w] = FishersLinearDiscriminant(train_patterns, train_targets, param, plot_on)
%Reshape the data points using the Fisher's linear discriminant
%Inputs:
% train_p
www.eeworm.com/read/330850/12864681
m ls.m
function [test_targets, w] = LS(train_patterns, train_targets, test_patterns, weights)
% Classify using the least-squares algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets
www.eeworm.com/read/330850/12864808
m perceptron_voted.m
function test_targets = Perceptron_Voted(train_patterns, train_targets, test_patterns, params)
% Classify using the Voted Perceptron algorithm
% Inputs:
% train_patterns - Train patterns
% trai
www.eeworm.com/read/330850/12864995
m components_without_df.m
function [test_targets, errors] = Components_without_DF(train_patterns, train_targets, test_patterns, Classifiers)
% Classify points using component classifiers without discriminant functions
% In
www.eeworm.com/read/330850/12865033
m fisherslineardiscriminant.m
function [patterns, train_targets, w] = FishersLinearDiscriminant(train_patterns, train_targets, param, plot_on)
%Reshape the data points using the Fisher's linear discriminant
%Inputs:
% train_p
www.eeworm.com/read/330850/12865212
m ml_ii.m
function test_targets = ML_II(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the ML-II algorithm. This function accepts as inputs the maximum number
% of Gaussians per
www.eeworm.com/read/317622/13500803
m ls.m
function [test_targets, w] = LS(train_patterns, train_targets, test_patterns, weights)
% Classify using the least-squares algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets