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/474600/6813398
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/474600/6813437
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/474600/6813497
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/474600/6813507
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/474600/6813515
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/286662/8751650
m cascade_correlation.m
function [test_targets, Wh, Wo, J] = Cascade_Correlation(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with the cascade-correlation algorithm
% I
www.eeworm.com/read/286662/8751738
m svm.m
function [test_targets, a_star] = SVM(train_patterns, train_targets, test_patterns, params)
% Classify using (a very simple implementation of) the support vector machine algorithm
%
% Inputs:
%
www.eeworm.com/read/286662/8751762
m ada_boost.m
function [test_targets, E] = ada_boost(train_patterns, train_targets, test_patterns, params)
% Classify using the AdaBoost algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets
www.eeworm.com/read/383433/8947396
m svm2.m
function [test_targets, a_star] = SVM2(train_patterns, train_targets, test_patterns, kernel, ker_param, solver, slack)
% Classify using (a very simple implementation of) the support vector machine
www.eeworm.com/read/372113/9521091
m cascade_correlation.m
function [test_targets, Wh, Wo, J] = Cascade_Correlation(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with the cascade-correlation algorithm
% I