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Error Bounds for lp-Norm Multiple Kernel Learning with Least Square Loss
Joint Authors
Source
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-08-05
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
The problem of learning the kernel function with linear combinations of multiple kernels has attracted considerable attention recently in machine learning.
Specially, by imposing an lp-norm penalty on the kernel combination coefficient, multiple kernel learning (MKL) was proved useful and effective for theoretical analysis and practical applications (Kloft et al., 2009, 2011).
In this paper, we present a theoretical analysis on the approximation error and learning ability of the lp-norm MKL.
Our analysis shows explicit learning rates for lp-norm MKL and demonstrates some notable advantages compared with traditional kernel-based learning algorithms where the kernel is fixed.
American Psychological Association (APA)
Lv, Shao-Gao& Zhu, Jin-De. 2012. Error Bounds for lp-Norm Multiple Kernel Learning with Least Square Loss. Abstract and Applied Analysis،Vol. 2012, no. 2012, pp.1-18.
https://search.emarefa.net/detail/BIM-507812
Modern Language Association (MLA)
Lv, Shao-Gao& Zhu, Jin-De. Error Bounds for lp-Norm Multiple Kernel Learning with Least Square Loss. Abstract and Applied Analysis No. 2012 (2012), pp.1-18.
https://search.emarefa.net/detail/BIM-507812
American Medical Association (AMA)
Lv, Shao-Gao& Zhu, Jin-De. Error Bounds for lp-Norm Multiple Kernel Learning with Least Square Loss. Abstract and Applied Analysis. 2012. Vol. 2012, no. 2012, pp.1-18.
https://search.emarefa.net/detail/BIM-507812
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-507812