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/372113/9521395
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/362008/10023759
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/362008/10023780
m nearest_neighbor.m
function test_targets = Nearest_Neighbor(train_patterns, train_targets, test_patterns, Knn)
% Classify using the Nearest neighbor algorithm
% Inputs:
% train_patterns - Train patterns
% train_t
www.eeworm.com/read/362008/10023848
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/362008/10023930
m local_polynomial.m
function test_targets = Local_Polynomial(train_patterns, train_targets, test_patterns, Nlp)
% Classify using the local polynomial fitting
% Inputs:
% train_patterns - Train patterns
% train_tar
www.eeworm.com/read/362008/10024041
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/361769/10036400
m id3.m
function D = ID3(train_features, train_targets, params, region)
% Classify using Quinlan's ID3 algorithm
% Inputs:
% features - Train features
% targets - Train targets
% params - [Number o
www.eeworm.com/read/360995/10070197
m dd_label.m
function z = dd_label(x,w,realoutput)
%DD_LABEL classify the dataset and put labels in the dataset
%
% Z = DD_LABEL(X,W)
%
% Compute the output labels of objects X by mapping through mapping W
% and
www.eeworm.com/read/357874/10199042
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/357874/10199054
m nearest_neighbor.m
function test_targets = Nearest_Neighbor(train_patterns, train_targets, test_patterns, Knn)
% Classify using the Nearest neighbor algorithm
% Inputs:
% train_patterns - Train patterns
% train_t