A new Support Vector classification algorithm with parametric-margin model

Author

Hao, Pei-Yi ; Tsai, Lung-Biao ; Lin, Min-Shiu

Author_Institution

Dept. of Inf. Manage., Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung

fYear

2008

fDate

1-8 June 2008

Firstpage

420

Lastpage

425

Abstract

In this paper, a new algorithm for Support Vector classification is described. It is shown how to use the parametric margin model with non-constant radius. This is useful in many cases, especially when the noise is heteroscedastic, that is, where it depends on x. Moreover, for a priori chosen v, the proposed new SV classification algorithm has advantage of using the parameter 0 les v les 1 on controlling the number of support vectors. To be more precise, v is an upper bound on the fraction of margin errors and a lower bound of the fraction of support vectors. Hence, the selection of v is more intuitive. The algorithm is analyzed theoretically and experimentally.

Keywords

pattern classification; support vector machines; heteroscedastic noise; parametric-margin model; support vector classification algorithm; support vector machine; Algorithm design and analysis; Classification algorithms; Function approximation; Information management; Pattern classification; Quadratic programming; Shape; Support vector machine classification; Support vector machines; Upper bound;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on

Conference_Location

Hong Kong

ISSN

1098-7576

Print_ISBN

978-1-4244-1820-6

Electronic_ISBN

1098-7576

Type

conf

DOI

10.1109/IJCNN.2008.4633826

Filename

4633826