Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Vol.38, No.1, 1/3 2002 A System Identification Method for Linear Regression Models
 

Summary: Vol.38, No.1, 1/3 2002
A System Identification Method for Linear Regression Models
Based on Support Vector Machine
Shuichi Adachi
, Tomonori Ogawa
and Ryugo konno
In this paper, a system identification method for linear regression models based on support vector machine is proposed.
It is shown through a numerical example that the proposed identification method is robust for input-output data with
outlier.
Key Words: support vector machine, support vector regression, system identification, regularization
1. Introduction
Support Vector Machines (SVM) proposed by V.N.
Vapnik early in 90's 1)
have become a subject of inten-
sive study in statistical learning theory. They have been
applied successfully to classification tasks and recently
extended to regression problems termed Support Vector
Regression (SVR) 2)
. In this paper, a brief introduction
to SVR is first presented, then a system identification

  

Source: Adachi, Shuichi - Electrical and Electronic Engineering, Faculity of Engineering, Utsunomiya University

 

Collections: Engineering