代码搜索：Boosting
找到约 146 项符合「Boosting」的源代码
代码结果 146
www.eeworm.com/read/177129/9468927
m locboost.m
function [D, P, theta, phi] = LocBoost(features, targets, params, region)
% Classify using the local boosting algorithm
% Inputs:
% features - Train features
% targets - Train targets
% par
www.eeworm.com/read/349842/10796866
m locboost.m
function [D, P, theta, phi] = LocBoost(features, targets, params, region)
% Classify using the local boosting algorithm
% Inputs:
% features - Train features
% targets - Train targets
% par
www.eeworm.com/read/316604/13520484
m locboost.m
function [D, P, theta, phi] = LocBoost(features, targets, params, region)
% Classify using the local boosting algorithm
% Inputs:
% features - Train features
% targets - Train targets
% par
www.eeworm.com/read/359185/6352551
m locboost.m
function [D, P, theta, phi] = LocBoost(features, targets, params, region)
% Classify using the local boosting algorithm
% Inputs:
% features - Train features
% targets - Train targets
% par
www.eeworm.com/read/493206/6398561
m locboost.m
function [D, P, theta, phi] = LocBoost(features, targets, params, region)
% Classify using the local boosting algorithm
% Inputs:
% features - Train features
% targets - Train targets
% par
www.eeworm.com/read/410924/11264982
m locboost.m
function [D, P, theta, phi] = LocBoost(features, targets, params, region)
% Classify using the local boosting algorithm
% Inputs:
% features - Train features
% targets - Train targets
% par
www.eeworm.com/read/131588/14136353
m locboost.m
function [D, P, theta, phi] = LocBoost(features, targets, params, region)
% Classify using the local boosting algorithm
% Inputs:
% features - Train features
% targets - Train targets
% par
www.eeworm.com/read/129915/14217749
m locboost.m
function [D, P, theta, phi] = LocBoost(features, targets, params, region)
% Classify using the local boosting algorithm
% Inputs:
% features - Train features
% targets - Train targets
% par
www.eeworm.com/read/415311/11077177
m locboost.m
function [D, P, theta, phi] = LocBoost(features, targets, params, region)
% Classify using the local boosting algorithm
% Inputs:
% features - Train features
% targets - Train targets
% par
www.eeworm.com/read/461042/1555315
m sbx_updlspm_ctc.m
function [BlkSize temp XSLArguments] = SBX_UPDLSPM_CTC(temp,IUC,Boosting,DataSize,BlkSize,FECType,PunctMode,ModMode,res,testcase,BurstNo,XSLArguments)
% Downlink Tx (BS) chain of user processing