An AdaBoost Algorithm with SVM Based on Nonlinear Decision Function

Author

Wu, Wei ; Yanan, Zhang ; Linlin, Wu

Author_Institution

Sch. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou, China

Volume

1

fYear

2009

fDate

6-7 June 2009

Firstpage

22

Lastpage

25

Abstract

This paper presents a method of using nonlinear decision function to improve the performance of AdaBoost with SVM based weak learners. Compared with the existing AdaBoostSVM methods, this method, named ERBF-AdaBoostSVM, has advantages of higher hate rate and better generalization performance. This method also provides non-linear separator in the weak learner space and classifies accurately more examples. Experimental results demonstrated that ERBF-AdaBoostSVM achieve better generalization performance and higher hate rate than the existing SVM and AdaBoostSVM methods.

Keywords

support vector machines; AdaBoost algorithm; ERBF-AdaBoostSVM; SVM based weak learner; generalization performance; higher hate rate; nonlinear decision function; nonlinear separator; Automation; Computational intelligence; Decision trees; Neural networks; Paper technology; Particle separators; Power engineering; Probability distribution; Support vector machine classification; Support vector machines; AdaBoost algorithm; SVM; nonlinear decision function;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-0-7695-3645-3

Type

conf

DOI

10.1109/CINC.2009.256

Filename

5231670