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
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;
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2012 7th IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4577-2118-2
DOI :
10.1109/ICIEA.2012.6360921
