An Improved Algorithm for Diverse AdaBoostSVM

Author

Guo, Song ; Gu, Guochang ; Liu, Haibo ; Shen, Jing ; Li, Changyou

Author_Institution

Sch. of Comput. Sci. & Technol., Harbin Eng. Univ., Harbin, China

Volume

1

fYear

2009

fDate

24-26 April 2009

Firstpage

839

Lastpage

842

Abstract

In order to improve the training convergence speed and detection accuracy of diverse AdaBoostSVM, an improved algorithm is proposed according to the asymmetry in face detection. In the algorithm, the weight of each weak learner, which represents importance of each weak learner, is determined by the error rate and the recognition capability of the weak learner for the face samples. The results of the experiments show that the proposed algorithm could improve the training convergence speed and the detection accuracy in face detection.

Keywords

convergence; face recognition; image sampling; support vector machines; detection accuracy; diverse AdaBoostSVM; error rate; face detection; face sample; recognition capability; training convergence speed; weak learner; Aerospace engineering; Algorithm design and analysis; Application software; Boosting; Computer science; Convergence; Error analysis; Face detection; Face recognition; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on

Conference_Location

Sanya, Hainan

Print_ISBN

978-0-7695-3605-7

Type

conf

DOI

10.1109/CSO.2009.452

Filename

5193822