Support vector regression estimation based on non-uniform lost function

Song Xiaofeng; Zhou Tong; Zhang Huanping

Support vector regression estimation based on non-uniform lost function

Song Xiaofeng, Zhou Tong, Zhang Huanping

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The performances of support vector regression estimation were analyzed. It was found that the insensitive factor ε can affect the performance of support vector regression estimation significantly. The noise inside the sample data should be considered in determining the insensitive factor ε 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.

Original language	English (US)
Title of host publication	Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Pages	1127-1130
Number of pages	4
State	Published - 2005
Externally published	Yes
Event	2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 - Shanghai, China Duration: Sep 1 2005 → Sep 4 2005

Publication series

Name	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	7 VOLS
ISSN (Print)	0589-1019

Other

Other	2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Country/Territory	China
City	Shanghai
Period	9/1/05 → 9/4/05

Keywords

Non-uniform lost function
Regression estimator
Support vector machine

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Cite this

Xiaofeng, S., Tong, Z., & Huanping, Z. (2005). Support vector regression estimation based on non-uniform lost function. In Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 (pp. 1127-1130). Article 1616619 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings; Vol. 7 VOLS).

Support vector regression estimation based on non-uniform lost function. / Xiaofeng, Song; Tong, Zhou; Huanping, Zhang.
Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005. 2005. p. 1127-1130 1616619 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings; Vol. 7 VOLS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Xiaofeng, S, Tong, Z & Huanping, Z 2005, Support vector regression estimation based on non-uniform lost function. in Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005., 1616619, Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, vol. 7 VOLS, pp. 1127-1130, 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005, Shanghai, China, 9/1/05.

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abstract = "The performances of support vector regression estimation were analyzed. It was found that the insensitive factor ε can affect the performance of support vector regression estimation significantly. The noise inside the sample data should be considered in determining the insensitive factor ε 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.",

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AB - The performances of support vector regression estimation were analyzed. It was found that the insensitive factor ε can affect the performance of support vector regression estimation significantly. The noise inside the sample data should be considered in determining the insensitive factor ε 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.

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ER -

Support vector regression estimation based on non-uniform lost function

Abstract

Publication series

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Keywords

ASJC Scopus subject areas

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