REGULARIZED LEAST SQUARE ALGORITHM WITH TWO KERNELS-Reference-Cited by-同舟云学术

REGULARIZED LEAST SQUARE ALGORITHM WITH TWO KERNELS

Published:2012-09 Issue:05 Volume:10 Page:1250043
ISSN:0219-6913
Container-title:International Journal of Wavelets, Multiresolution and Information Processing
language:en
Short-container-title:Int. J. Wavelets Multiresolut Inf. Process.

Author:

SUN HONGWEI¹,LIU PING²

Affiliation:

1. School of Mathematic Science, University of Jinan, Shandong Provincial Key Laboratory of Network-Based Intelligent Computing, Jinan 250022, P. R. China

2. No. 1 Middle School of LiJing, Dongying 257400, P. R. China

Abstract

A new multi-kernel regression learning algorithm is studied in this paper. In our setting, the hypothesis space is generated by two Mercer kernels, thus it has stronger approximation ability than the single kernel case. We provide the mathematical foundation for this regularized learning algorithm. We obtain satisfying capacity-dependent error bounds and learning rates by the covering number method.

Publisher

World Scientific Pub Co Pte Lt

Subject

Applied Mathematics,Information Systems,Signal Processing

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0219691312500439

Reference19 articles.

1. Theory of reproducing kernels

2. On regularization algorithms in learning theory

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