dc.contributor.authorLi, Guoqi.
dc.contributor.authorZhao, Guangshe.
dc.date.accessioned2013-07-22T06:23:07Z
dc.date.available2013-07-22T06:23:07Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.citationLi, G., & Zhao, G. (2012). Online learning with kernels in classification and regression. 2012 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS).en_US
dc.identifier.urihttp://hdl.handle.net/10220/11985
dc.description.abstractNew 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.en_US
dc.language.isoenen_US
dc.rights© 2012 IEEE.en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering
dc.titleOnline learning with kernels in classification and regressionen_US
dc.typeConference Paper
dc.contributor.conferenceIEEE Conference on Evolving and Adaptive Intelligent Systems (2012 : Madrid, Spain)en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/EAIS.2012.6232798


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