DocumentCode
2336553
Title
A new method of online learning with kernels for regression
Author
Li, Guoqi ; Wen, Changyun ; Cui, Dongyao ; Yang, Feng
Author_Institution
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
fYear
2012
fDate
18-20 July 2012
Firstpage
1291
Lastpage
1296
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.
Keywords
Hilbert spaces; learning (artificial intelligence); optimisation; pattern classification; regression analysis; constrained optimization model; kernels; memory requirement; online learning; regression; reproducing kernel Hilbert space; Algorithm design and analysis; Hilbert space; Kernel; Machine learning; Memory management; Optimization; Support vector machines; Classification; Kernels; Online Learning; Regression; Reproducing Kernel Hilbert Space;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Electronics and Applications (ICIEA), 2012 7th IEEE Conference on
Conference_Location
Singapore
Print_ISBN
978-1-4577-2118-2
Type
conf
DOI
10.1109/ICIEA.2012.6360921
Filename
6360921
Link To Document