A Simpler Approach to Coefficient Regularized Support Vector Machines Regression

Hongzhi Tong, Di-Rong Chen and Fenghong Yang

Abstract and Applied Analysis, 2014, vol. 2014, 1-8

Abstract:

We consider a kind of support vector machines regression (SVMR) algorithms associated with 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.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/AAA/2014/206015.pdf (application/pdf)
http://downloads.hindawi.com/journals/AAA/2014/206015.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlaaa:206015

DOI: 10.1155/2014/206015

Access Statistics for this article

More articles in Abstract and Applied Analysis from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().