An Efficient Kernel Learning Algorithm for Semisupervised Regression Problems
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-09-08
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhang, Chao& Lv, Shaogao. 2015. An Efficient Kernel Learning Algorithm for Semisupervised Regression Problems. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1073861
Modern Language Association (MLA)
Zhang, Chao& Lv, Shaogao. An Efficient Kernel Learning Algorithm for Semisupervised Regression Problems. Mathematical Problems in Engineering No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1073861
American Medical Association (AMA)
Zhang, Chao& Lv, Shaogao. An Efficient Kernel Learning Algorithm for Semisupervised Regression Problems. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1073861
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1073861