Online learning with kernels in classification and regression

New optimization models and algorithms for online learning with kernels (OLK) in classification and 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...

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Main Authors:	Li, Guoqi., Zhao, Guangshe.
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/10356/84635 http://hdl.handle.net/10220/11985
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-84635
record_format	dspace
spelling	sg-ntu-dr.10356-846352020-03-07T13:24:44Z Online learning with kernels in classification and regression Li, Guoqi. Zhao, Guangshe. School of Electrical and Electronic Engineering IEEE Conference on Evolving and Adaptive Intelligent Systems (2012 : Madrid, Spain) DRNTU::Engineering::Electrical and electronic engineering New optimization models and algorithms for online learning with kernels (OLK) in classification and 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. 2013-07-22T06:23:07Z 2019-12-06T15:48:45Z 2013-07-22T06:23:07Z 2019-12-06T15:48:45Z 2012 2012 Conference Paper Li, G., & Zhao, G. (2012). Online learning with kernels in classification and regression. 2012 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS). https://hdl.handle.net/10356/84635 http://hdl.handle.net/10220/11985 10.1109/EAIS.2012.6232798 en © 2012 IEEE.
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. Zhao, Guangshe. Online learning with kernels in classification and regression
description	New optimization models and algorithms for online learning with kernels (OLK) in classification and 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. Zhao, Guangshe.
format	Conference or Workshop Item
author	Li, Guoqi. Zhao, Guangshe.
author_sort	Li, Guoqi.
title	Online learning with kernels in classification and regression
title_short	Online learning with kernels in classification and regression
title_full	Online learning with kernels in classification and regression
title_fullStr	Online learning with kernels in classification and regression
title_full_unstemmed	Online learning with kernels in classification and regression
title_sort	online learning with kernels in classification and regression
publishDate	2013
url	https://hdl.handle.net/10356/84635 http://hdl.handle.net/10220/11985
_version_	1681042104011194368

Online learning with kernels in classification and regression

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