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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 451947, 9 pages
http://dx.doi.org/10.1155/2015/451947
Research Article

An Efficient Kernel Learning Algorithm for Semisupervised Regression Problems

Statistics School, Southwestern University of Finance and Economics, Chengdu 611130, China

Received 4 July 2015; Accepted 25 August 2015

Academic Editor: Igor Andrianov

Copyright © 2015 Chao Zhang and Shaogao Lv. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Kernel selection is a central issue in kernel methods of machine learning. In this paper, we investigate the regularized learning schemes based on kernel design methods. Our ideal kernel is derived from a simple iterative procedure using large scale unlabeled data in a semisupervised framework. Compared with most of existing approaches, our algorithm avoids multioptimization in the process of learning kernels and its computation is as efficient as the standard single kernel-based algorithms. Moreover, large amounts of information associated with input space can be exploited, and thus generalization ability is improved accordingly. We provide some theoretical support for the least square cases in our settings; also these advantages are shown by a simulation experiment and a real data analysis.