Support vectors selection by linear programming

Author

Kecman, Vojislav ; Hadzic, Ivana

Author_Institution

Dept. of Mech. Eng., Auckland Univ., New Zealand

Volume

5

fYear

2000

fDate

2000

Firstpage

193

Abstract

A linear programming (LP) based method is proposed for learning from experimental data in solving the nonlinear regression and classification problems. LP controls both the number of basis functions in a neural network (i.e., support vector machine) and the accuracy of the learning machine. Two different methods are suggested in regression and their equivalence is discussed. Examples of function approximation and classification (pattern recognition) illustrate the efficiency of the proposed method

Keywords

function approximation; learning (artificial intelligence); linear programming; neural nets; pattern classification; statistical analysis; basis functions; function approximation; learning from experimental data; learning machine; linear programming; neural network; nonlinear regression; pattern classification; pattern recognition; support vector machine; support vector selection; Function approximation; Linear programming; Machine learning; Mechanical engineering; Minimization methods; Neural networks; Pattern recognition; Quadratic programming; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on

Conference_Location

Como

ISSN

1098-7576

Print_ISBN

0-7695-0619-4

Type

conf

DOI

10.1109/IJCNN.2000.861456

Filename

861456