DocumentCode
2736707
Title
Boosting the PLS Algorithm for Regressive Modelling
Author
Yu, Ling ; Wu, Tiejun
Author_Institution
Dept. of Control Sci. & Eng., Zhejiang Univ., Hangzhou
Volume
1
fYear
0
fDate
0-0 0
Firstpage
4833
Lastpage
4836
Abstract
Boosting algorithms are a class of general methods used to improve the generalization performance of regression analysis. The main idea is to maintain a distribution over the train set. In order to use the given distribution directly, a modified PLS algorithm is proposed and used as the base learner to deal with regression problems. Experiments on gasoline octane number prediction demonstrate that boosting the modified PLS algorithm has better generalization performance over the PLS algorithm
Keywords
generalisation (artificial intelligence); learning (artificial intelligence); least squares approximations; regression analysis; boosting; gasoline octane number prediction; generalization; partial least squares; regression analysis; Automation; Boosting; Boosting; generalization; partial least square (PLS); regression;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location
Dalian
Print_ISBN
1-4244-0332-4
Type
conf
DOI
10.1109/WCICA.2006.1713302
Filename
1713302
Link To Document