A Simpler Approach to Coefficient Regularized Support Vector Machines Regression
Joint Authors
Yang, Fenghong
Chen, Di-Rong
Tong, Hongzhi
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-05-26
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
We consider a kind of support vector machines regression (SVMR) algorithms associated with l q ( 1 ≤ q < ∞ ) coefficient-based regularization and data-dependent hypothesis space.
Compared with former literature, we provide here a simpler convergence analysis for those algorithms.
The novelty of our analysis lies in the estimation of the hypothesis error, which is implemented by setting a stepping stone between the coefficient regularized SVMR and the classical SVMR.
An explicit learning rate is then derived under very mild conditions.
American Psychological Association (APA)
Tong, Hongzhi& Chen, Di-Rong& Yang, Fenghong. 2014. A Simpler Approach to Coefficient Regularized Support Vector Machines Regression. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1013471
Modern Language Association (MLA)
Tong, Hongzhi…[et al.]. A Simpler Approach to Coefficient Regularized Support Vector Machines Regression. Abstract and Applied Analysis No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1013471
American Medical Association (AMA)
Tong, Hongzhi& Chen, Di-Rong& Yang, Fenghong. A Simpler Approach to Coefficient Regularized Support Vector Machines Regression. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1013471
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1013471