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Online learning with kernels in classification and regression

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2 Author(s)
Guoqi Li ; Sch. of EEE, Nanyang Technol. Univ., Singapore, Singapore ; Guangshe Zhao

New optimization models and algorithms for online learning with kernels (OLK) in classification and 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.

Published in:

Evolving and Adaptive Intelligent Systems (EAIS), 2012 IEEE Conference on

Date of Conference:

17-18 May 2012