Reduced sets and fast approximation for kernel methods

Author

Zheng, Da-Nian ; Wang, Jia-Xin ; Zhao, Yan-Nan ; Yang, Ze-Hong

Author_Institution

Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China

Volume

7

fYear

2005

fDate

18-21 Aug. 2005

Firstpage

4237

Abstract

Kernel methods often need much computational time in their testing stages due to large numbers of support vectors, especially in dealing with some large datasets. This paper presents two reduced set approaches - reduced set selection (RSS) and reduced set construction (RSC) to fast approximate the kernel methods, and compares their performances on the benchmark repository. Experimental results demonstrate that both the two approaches can speed up the kernel methods greatly, while RSC behaves better than RSS in the most cases.

Keywords

data reduction; pattern classification; set theory; support vector machines; fast approximation; kernel methods; reduced set construction; reduced set selection; support vectors; Benchmark testing; Computer science; Equations; Greedy algorithms; Iterative algorithms; Kernel; Pattern classification; Polynomials; Support vector machine classification; Support vector machines; Reduced set selection; fast approximation; kernel methods; reduced set construction;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on

Conference_Location

Guangzhou, China

Print_ISBN

0-7803-9091-1

Type

conf

DOI

10.1109/ICMLC.2005.1527681

Filename

1527681