The theoretical analysis of kernel technique and its applications

Author

Tao, Qing ; Wang, Jiaqi ; Wu, Gaowei ; Wang, Jue

Author_Institution

Inst. of Autom., Acad. Sinica, Beijing, China

Volume

1

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

571

Lastpage

576

Abstract

Investigates linear separability in feature space from Tietze extension theorem and function approximation theory, and kernel technique is motivated theoretically. Two kernel-based algorithms are presented. One of them is a general learning algorithm for feedforward neural networks, and they can solve large-scale classification problems

Keywords

feedforward neural nets; function approximation; learning (artificial intelligence); learning automata; pattern classification; quadratic programming; radial basis function networks; Schauder basis; Tietze extension theorem; feature mappings; feedforward neural networks; function approximation theory; general learning algorithm; kernel covering approach; kernel mappings; kernel technique; large-scale classification problems; linear separability; support vectors; Approximation methods; Automation; Feedforward neural networks; Function approximation; Kernel; Large-scale systems; Neural networks; Projection algorithms; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1005535

Filename

1005535