Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/102001
Title: A new method of online learning with kernels for regression
Authors: Li, Guoqi
Wen, Changyun
Cui, Dongyao
Yang, Feng
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
http://hdl.handle.net/10220/12713
DOI: 10.1109/ICIEA.2012.6360921
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

Page view(s) 50

392
Updated on May 11, 2021

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.