代码搜索：Classify
找到约 2,639 项符合「Classify」的源代码
代码结果 2,639
www.eeworm.com/read/349842/10796957
m em.m
function [D, param_struct] = EM(train_features, train_targets, Ngaussians, region)
% Classify using the expectation-maximization algorithm
% Inputs:
% features - Train features
% targets -
www.eeworm.com/read/349842/10797001
m perceptron_vim.m
function D = Perceptron_VIM(train_features, train_targets, params, region)
% Classify using the variable incerement Perceptron with margin algorithm
% Inputs:
% features - Train features
% tar
www.eeworm.com/read/397106/8067531
m parzen.m
function D = parzen(train_features, train_targets,params, region)
% Classify using the Parzen windows algorithm
% Inputs:
% features - Train features
% targets - Train targets
% param - [hPar
www.eeworm.com/read/397106/8067598
m svm.m
function D = support_vectors(train_features, train_targets, params, region);
% Classify using the Support Vectors algorithm
% Inputs:
% features - Train features
% targets - Train targe
www.eeworm.com/read/397106/8067707
m local_polynomial.m
function D = Local_Polynomial(features, targets, Nlp, region)
% Classify using the local polynomial fitting
% Inputs:
% features - Train features
% targets - Train targets
% Nlp - Number of test po
www.eeworm.com/read/397106/8067817
m em.m
function D = EM(train_features, train_targets, Ngaussians, region)
% Classify using the expectation-maximization algorithm
% Inputs:
% features - Train features
% targets - Train targets
% Ngaussia
www.eeworm.com/read/142729/12929532
m classifyer_angel_ica.m
function [ClassEstimats,ClassEstimatsRec]=classifyer_angel_ica(S,Frac);
% Classify the output of ICA using the largest value for a given document
% as class.
if nargin
www.eeworm.com/read/316604/13520389
m nearestneighborediting.m
function D = NearestNeighborEditing(train_features, train_targets, params, region)
% Classify points using the nearest neighbor editing algorithm
% Inputs:
% train_features - Train features
% t
www.eeworm.com/read/316604/13520396
m perceptron_bvi.m
function D = Perceptron_BVI(train_features, train_targets, params, region)
% Classify using the batch variable increment Perceptron algorithm
% Inputs:
% features - Train features
% targets
www.eeworm.com/read/316604/13520412
m perceptron_batch.m
function D = Perceptron_Batch(train_features, train_targets, params, region)
% Classify using the batch Perceptron algorithm
% Inputs:
% features - Train features
% targets - Train targets