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|Title:||A new method of online learning with kernels for regression||Authors:||Li, Guoqi
|Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2011||Abstract:||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.||URI:||https://hdl.handle.net/10356/102001
|DOI:||10.1109/ICIEA.2012.6360921||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Conference Papers|
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