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/142518/12942184
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/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
www.eeworm.com/read/317622/13500815
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/317622/13500853
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/317622/13500900
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/317622/13500971
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/316604/13520425
m nddf.m
function [D, g0, g1] = NDDF(train_features, train_targets, cost, region, test_feature)
% Classify using the normal density discriminant function
% Inputs:
% features - Train features
% target
www.eeworm.com/read/426742/6330835
txt id3matlab.txt
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/359185/6352518
m nddf.m
function [D, g0, g1] = NDDF(train_features, train_targets, cost, region, test_feature)
% Classify using the normal density discriminant function
% Inputs:
% features - Train features
% target
www.eeworm.com/read/493206/6398496
m nddf.m
function [D, g0, g1] = NDDF(train_features, train_targets, cost, region, test_feature)
% Classify using the normal density discriminant function
% Inputs:
% features - Train features
% target