Training support vector machines: an application to well-log data classification

Author

Hui, Yan ; Xuegong, Zhang ; Xianda, Zhang

Author_Institution

Dept. of Autom., Tsinghua Univ., Beijing, China

Volume

3

fYear

2000

fDate

2000

Firstpage

1427

Abstract

In this paper, we investigate the application of support vector machines (SVM) in pattern recognition. SVM is a learning technique developed by Vapnik et al. (1997) that can be seen as a new method for training polynomial, neural network, or radial basis functions classifiers. The decision surfaces are found by solving a linearly constrained quadratic programming problem. We present experimental results of our implementation of SVM, and demonstrate its advantage on well-log data classification problem

Keywords

geophysical techniques; learning automata; neural nets; pattern classification; polynomials; quadratic programming; SVM; learning technique; linearly constrained quadratic programming problem; neural network classifiers; pattern recognition; polynomial classifiers; radial basis function classifiers; support vector machines; training; well-log data classification; Automation; Intelligent systems; Laboratories; Learning systems; Neural networks; Pattern recognition; Risk management; Statistical learning; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-5747-7

Type

conf

DOI

10.1109/ICOSP.2000.893369

Filename

893369