A new method of online learning with kernels for regression

New optimization models and algorithms for online learning with kernels (OLK) in regression are proposed in a Reproducing Kernel Hilbert Space (RKHS) by solving a constrained optimization model. The “forgetting” factor in the model makes it possible that the memory requirement of the algorithm can b...

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Main Authors:	Li, Guoqi, Wen, Changyun, Cui, Dongyao, Yang, Feng
Other Authors:	School of Electrical and Electronic Engineering
Format:	Conference or Workshop Item
Language:	English
Published:	2013
Subjects:	DRNTU::Engineering::Electrical and electronic engineering
Online Access:	https://hdl.handle.net/10356/102001 http://hdl.handle.net/10220/12713
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-1020012020-03-07T13:24:51Z A new method of online learning with kernels for regression Li, Guoqi Wen, Changyun Cui, Dongyao Yang, Feng School of Electrical and Electronic Engineering IEEE Conference on Industrial Electronics and Applications (7th : 2012 : Singapore) DRNTU::Engineering::Electrical and electronic engineering New optimization models and algorithms for online learning with kernels (OLK) in regression are proposed in a Reproducing Kernel Hilbert Space (RKHS) by solving a constrained optimization model. The “forgetting” factor in the model makes it possible that the memory requirement of the algorithm can be bounded as the learning process continues. The applications of the proposed OLK algorithms in classification and regression show their effectiveness in comparing with the state of art algorithms. 2013-08-01T02:31:00Z 2019-12-06T20:48:13Z 2013-08-01T02:31:00Z 2019-12-06T20:48:13Z 2011 2011 Conference Paper https://hdl.handle.net/10356/102001 http://hdl.handle.net/10220/12713 10.1109/ICIEA.2012.6360921 en
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering Li, Guoqi Wen, Changyun Cui, Dongyao Yang, Feng A new method of online learning with kernels for regression
description	New optimization models and algorithms for online learning with kernels (OLK) in regression are proposed in a Reproducing Kernel Hilbert Space (RKHS) by solving a constrained optimization model. The “forgetting” factor in the model makes it possible that the memory requirement of the algorithm can be bounded as the learning process continues. The applications of the proposed OLK algorithms in classification and regression show their effectiveness in comparing with the state of art algorithms.
author2	School of Electrical and Electronic Engineering
author_facet	School of Electrical and Electronic Engineering Li, Guoqi Wen, Changyun Cui, Dongyao Yang, Feng
format	Conference or Workshop Item
author	Li, Guoqi Wen, Changyun Cui, Dongyao Yang, Feng
author_sort	Li, Guoqi
title	A new method of online learning with kernels for regression
title_short	A new method of online learning with kernels for regression
title_full	A new method of online learning with kernels for regression
title_fullStr	A new method of online learning with kernels for regression
title_full_unstemmed	A new method of online learning with kernels for regression
title_sort	new method of online learning with kernels for regression
publishDate	2013
url	https://hdl.handle.net/10356/102001 http://hdl.handle.net/10220/12713
_version_	1681041070746501120

A new method of online learning with kernels for regression

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