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/493206/6398571
asv id3.asv
function D = ID3(train_features, train_targets, params, region)
% Classify using Quinlan's ID3 algorithm
% Inputs:
% features - Train features
% targets - Train targets
% params - [Number
www.eeworm.com/read/410924/11264801
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
www.eeworm.com/read/410924/11265004
asv id3.asv
function D = ID3(train_features, train_targets, params, region)
% Classify using Quinlan's ID3 algorithm
% Inputs:
% features - Train features
% targets - Train targets
% params - [Number
www.eeworm.com/read/405069/11472156
m parzen.m
function test_targets = parzen(train_patterns, train_targets, test_patterns, hn)
% Classify using the Parzen windows algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets - Trai
www.eeworm.com/read/405069/11472169
m discrete_bayes.m
function test_targets = Discrete_Bayes(train_patterns, train_targets, test_patterns, cost)
% Classify discrete patterns using the Bayes decision theory
% Inputs:
% train_patterns - Train pattern
www.eeworm.com/read/405069/11472211
m projection_pursuit.m
function [test_targets, V, Wo] = Projection_Pursuit(train_patterns, train_targets, test_patterns, Ncomponents)
% Classify using projection pursuit regression
% Inputs:
% train_patterns - Train p
www.eeworm.com/read/405069/11472256
m balanced_winnow.m
function [test_targets, a_plus, a_minus] = Balanced_Winnow(train_patterns, train_targets, test_patterns, params)
% Classify using the balanced Winnow algorithm
% Inputs:
% training_patterns -
www.eeworm.com/read/405069/11472268
m store_grabbag.m
function test_targets = Store_Grabbag(train_patterns, train_targets, test_patterns, Knn)
% Classify using the store-grabbag algorithm (an improvement on the nearest neighbor)
% Inputs:
% train_p
www.eeworm.com/read/405069/11472291
m pnn.m
function test_targets = PNN(train_patterns, train_targets, test_patterns, sigma)
% Classify using a probabilistic neural network
% Inputs:
% train_patterns - Train patterns
% train_targets - Tr
www.eeworm.com/read/405069/11472300
m em.m
function [test_targets, param_struct] = EM(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the expectation-maximization algorithm
% Inputs:
% train_patterns - Train pa