Towards the online learning with Kernels in classification and regression

被引：4

作者：

Li G. ^{[1
]}

Zhao G. ^{[2
]}

Yang F. ^{[3
]}

机构：

[1] School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

[2] School of Aerospace Xi'an, Jiaotong University, Xi'an, Shaanxi

[3] Department of Computing Science, Institute of High Performance Computing, Singapore, 138632, AStar

来源：

Evolving Systems | 2014年 / 5卷 / 01期

关键词：

Bounded memory requirement reproducing Kernel Hilbert space; Classification; Kernels; Online learning; Regression;

D O I：

10.1007/s12530-013-9090-9

中图分类号：

学科分类号：

摘要：

In this paper, 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. © 2013 Springer-Verlag Berlin Heidelberg."

引用

页码：11 / 19

页数：8

共 16 条

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