SVM training time reduction using vector quantization

Author

Lebrun, Gilles ; Charrier, Christophe ; Cardot, Hubert

Author_Institution

Groupe Vision et Anal. d´´Image, LUSAC, Saint-Lo, France

Volume

1

fYear

2004

fDate

23-26 Aug. 2004

Firstpage

160

Abstract

In this paper, we describe a new method for training SVM on large data sets. Vector quantization is applied to reduce a large data set by replacing examples by prototypes. Training time for choosing optimal parameters is greatly reduced. Some experimental results yields to demonstrate that this method can reduce training time by a factor of 100, while preserving classification rate. Moreover this method allows to find a decision function with a low complexity when the training data set includes noisy or error examples.

Keywords

computational complexity; learning (artificial intelligence); pattern classification; support vector machines; vector quantisation; SVM training time reduction; classification rate; computational complexity; decision function; optimal parameters; prototypes; training data set; vector quantization; Databases; Decoding; Kernel; Noise reduction; Pattern recognition; Prototypes; Support vector machine classification; Support vector machines; Training data; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on

ISSN

1051-4651

Print_ISBN

0-7695-2128-2

Type

conf

DOI

10.1109/ICPR.2004.1334035

Filename

1334035