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/474600/6813499
asv bagging.asv
function [test_targets] = Bagging(train_patterns, train_targets, test_patterns, params)
% Classify using the Bagging algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets - Trai
www.eeworm.com/read/474600/6813510
m locboost.m
function [test_targets, P, theta, phi] = LocBoost(train_patterns, train_targets, test_patterns, params)
% Classify using the local boosting algorithm
% Inputs:
% train_patterns - Train patterns
www.eeworm.com/read/474600/6813522
m lms.m
function [test_targets, updates] = LMS(train_patterns, train_targets, test_patterns, params)
% Classify using the least means square algorithm
% Inputs:
% train_patterns - Train patterns
% trai
www.eeworm.com/read/474600/6813555
m genetic_algorithm.m
function test_targets = Genetic_Algorithm(train_patterns, train_targets, test_patterns, params)
% Classify using a basic genetic algorithm
% Inputs:
% training_patterns - Train patterns
% tra
www.eeworm.com/read/474600/6813574
m bagging.m
function [test_targets] = Bagging(train_patterns, train_targets, test_patterns, params)
% Classify using the Bagging algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets - Trai
www.eeworm.com/read/474600/6813577
m basebagging.m
function test_targets = BaseBagging(train_patterns, train_targets, test_patterns, params)
% Classify using the Bagging algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets - Tr
www.eeworm.com/read/474600/6813578
m relaxation_ssm.m
function [test_targets, a] = Relaxation_SSM(train_patterns, train_targets, test_patterns, params)
% Classify using the single-sample relaxation with margin algorithm
% Inputs:
% train_patterns -
www.eeworm.com/read/395876/8147348
java compareints.java
// control/CompareInts.java
// TIJ4 Chapter Control, Exercise 2, page 139
/* Write a program that generates 25 random int values. For each value, use an
* if-else statement to classify it as greate
www.eeworm.com/read/294739/8209459
txt jueceshu.txt
matlab 决策树cart算法源代码
function D = CART(train_features, train_targets, params, region)
% Classify using classification and regression trees
% Inputs:
% features - Train features
% targets -
www.eeworm.com/read/294005/8258158
conf sample-l7-filter.conf
# The format of this file is:
# protocol mark
# Do not use marks less than 3, since a mark of 0 means that l7-filter hasn't
# seen the packet yet, and a mark of 1 means that it has failed to classify