Incremental support vector machines for handwritten Arabic character recognition

Author

Bentounsi, H. ; Batouche, M.

Author_Institution

Dept. de l´´lnformatique, Univ. Mentouri, Constantine, Algeria

fYear

2004

fDate

19-23 April 2004

Firstpage

477

Lastpage

478

Abstract

In this paper, the use of support vector machines (SVM) for handwritten Arabic character recognition is studied. SVMs are based on structural risk minimization, which tries to maximize the generalization capability on the unseen data by reducing the empirical risk on the seen data. SVMs are able to summarize the data space in a very concise manner as support vectors, for incremental learning by preserving at each step of training the resulting support vectors is used. The model obtained by this method is the same or similar that has been obtained using all the data.

Keywords

feature extraction; handwritten character recognition; learning (artificial intelligence); support vector machines; SVM; handwritten Arabic character recognition; incremental learning; machine learning; structural risk minimization; support vector machines; Artificial neural networks; Character recognition; Handwriting recognition; Kernel; Machine learning; Pattern recognition; Risk management; Statistical learning; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Communication Technologies: From Theory to Applications, 2004. Proceedings. 2004 International Conference on

Print_ISBN

0-7803-8482-2

Type

conf

DOI

10.1109/ICTTA.2004.1307839

Filename

1307839