A new method of online learning with kernels for regression
Date of Issue2011
IEEE Conference on Industrial Electronics and Applications (7th : 2012 : Singapore)
School of Electrical and Electronic Engineering
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.
DRNTU::Engineering::Electrical and electronic engineering