Preliminary Study on Additive Radial Basis Function Networks

Shih-Hui Liao; Chin-Teng Lin; Jyh Yeong Chang

doi:10.1109/ICSMC.2010.5641719

Preliminary Study on Additive Radial Basis Function Networks

Shih-Hui Liao, Chin-Teng Lin, Jyh Yeong Chang

Department of Electrical and Computer Engineering

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

Abstract

In this paper, a new class of learning models, namely the additive radial basis function networks (ARBFNs) for general nonlinear regression problems are proposed. This class of learning machines combines the radial basis function networks (RBFNs) commonly used in general machine learning problems and the additive models (AMs) frequently encountered in semi parametric regression problems. In statistical regression theory, AM is a good compromise between the linear parametric model and the non parametric model. Simulation results show that for the given learning problem, ARBFNs usually need fewer hidden nodes than those of RBFNs for the same level of accuracy.

Original language	English
Title of host publication	IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2010)
Publisher	IEEE
Pages	3113-3117
Number of pages	5
ISBN (Print)	978-1-4244-6588-0
DOIs	https://doi.org/10.1109/ICSMC.2010.5641719
State	Published - 2010
Event	2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010 - Istanbul, Turkey Duration: 10 Oct 2010 → 13 Oct 2010

Publication series

Name	IEEE International Conference on Systems Man and Cybernetics Conference Proceedings
ISSN (Print)	1062-922X

Conference

Conference	2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
Country	Turkey
City	Istanbul
Period	10/10/10 → 13/10/10

Keywords

additive radial basis function network (ARBFN); radial basis function network (RBFN); additive model (AM); semi parametric regression

Access to Document

10.1109/ICSMC.2010.5641719

Fingerprint Dive into the research topics of 'Preliminary Study on Additive Radial Basis Function Networks'. Together they form a unique fingerprint.

Cite this

@inproceedings{058528b8e919451698ae46d06ba585d1,

title = "Preliminary Study on Additive Radial Basis Function Networks",

abstract = "In this paper, a new class of learning models, namely the additive radial basis function networks (ARBFNs) for general nonlinear regression problems are proposed. This class of learning machines combines the radial basis function networks (RBFNs) commonly used in general machine learning problems and the additive models (AMs) frequently encountered in semi parametric regression problems. In statistical regression theory, AM is a good compromise between the linear parametric model and the non parametric model. Simulation results show that for the given learning problem, ARBFNs usually need fewer hidden nodes than those of RBFNs for the same level of accuracy.",

keywords = "additive radial basis function network (ARBFN); radial basis function network (RBFN); additive model (AM); semi parametric regression",

author = "Shih-Hui Liao and Chin-Teng Lin and Chang, {Jyh Yeong}",

year = "2010",

doi = "10.1109/ICSMC.2010.5641719",

language = "English",

isbn = "978-1-4244-6588-0",

series = "IEEE International Conference on Systems Man and Cybernetics Conference Proceedings",

publisher = "IEEE",

pages = "3113--3117",

booktitle = "IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2010)",

note = "null ; Conference date: 10-10-2010 Through 13-10-2010",

}

Liao, S-H, Lin, C-T & Chang, JY 2010, Preliminary Study on Additive Radial Basis Function Networks. in IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2010). IEEE International Conference on Systems Man and Cybernetics Conference Proceedings, IEEE, pp. 3113-3117, 2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010, Istanbul, Turkey, 10/10/10. https://doi.org/10.1109/ICSMC.2010.5641719

Preliminary Study on Additive Radial Basis Function Networks. / Liao, Shih-Hui; Lin, Chin-Teng; Chang, Jyh Yeong.

IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2010). IEEE, 2010. p. 3113-3117 (IEEE International Conference on Systems Man and Cybernetics Conference Proceedings).

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

TY - GEN

T1 - Preliminary Study on Additive Radial Basis Function Networks

AU - Liao, Shih-Hui

AU - Lin, Chin-Teng

AU - Chang, Jyh Yeong

PY - 2010

Y1 - 2010

N2 - In this paper, a new class of learning models, namely the additive radial basis function networks (ARBFNs) for general nonlinear regression problems are proposed. This class of learning machines combines the radial basis function networks (RBFNs) commonly used in general machine learning problems and the additive models (AMs) frequently encountered in semi parametric regression problems. In statistical regression theory, AM is a good compromise between the linear parametric model and the non parametric model. Simulation results show that for the given learning problem, ARBFNs usually need fewer hidden nodes than those of RBFNs for the same level of accuracy.

AB - In this paper, a new class of learning models, namely the additive radial basis function networks (ARBFNs) for general nonlinear regression problems are proposed. This class of learning machines combines the radial basis function networks (RBFNs) commonly used in general machine learning problems and the additive models (AMs) frequently encountered in semi parametric regression problems. In statistical regression theory, AM is a good compromise between the linear parametric model and the non parametric model. Simulation results show that for the given learning problem, ARBFNs usually need fewer hidden nodes than those of RBFNs for the same level of accuracy.

KW - additive radial basis function network (ARBFN); radial basis function network (RBFN); additive model (AM); semi parametric regression

U2 - 10.1109/ICSMC.2010.5641719

DO - 10.1109/ICSMC.2010.5641719

M3 - Conference contribution

SN - 978-1-4244-6588-0

T3 - IEEE International Conference on Systems Man and Cybernetics Conference Proceedings

SP - 3113

EP - 3117

BT - IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2010)

PB - IEEE

Y2 - 10 October 2010 through 13 October 2010

ER -