Support Vector Regression Estimation Based on Non-uniform Lost Function

Author

Song, Xiaofeng ; Zhou, Tong ; Zhang, Huanping

fYear

2006

Firstpage

1127

Lastpage

1130

Abstract

The performances of support vector regression estimation were analyzed. It was found that the insensitive factor epsiv can affect the performance of support vector regression estimation significantly. The noise inside the sample data should be considered in determining the insensitive factor epsiv when support vector regression was employed. A novel support vector regression based on non-uniform lost function (NLF-SVR) was proposed to deal with different noise data density function in different region. The formulation and algorithms of computing NLF-SVR were given. The test example showed that the outcomes of NLF-SVR are better than that of conventional SVR. NLF-SVR can be applied in physiological systems modeling

Keywords

biology computing; estimation theory; noise; physiological models; regression analysis; support vector machines; noise data density function; nonuniform lost function; physiological systems modeling; support vector regression estimation; Biomedical engineering; Density functional theory; Machine learning; Machine learning algorithms; Modeling; Neural networks; Permission; Statistics; Support vector machines; Testing; non-uniform lost function; regression estimator; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the

Conference_Location

Shanghai

Print_ISBN

0-7803-8741-4

Type

conf

DOI

10.1109/IEMBS.2005.1616619

Filename

1616619