Indefinite Kernel Network with -Norm Regularization

Zhongfeng Qu and Hongwei Sun

Discrete Dynamics in Nature and Society, 2016, vol. 2016, 1-6

Abstract:

We study the asymptotical properties of indefinite kernel network with -norm regularization. The framework under investigation is different from classical kernel learning. Positive semidefiniteness is not required by the kernel function. By a new step stone technique, without any interior cone condition for input space and condition for the probability measure , satisfied error bounds and learning rates are deduced.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/DDNS/2016/6516258.pdf (application/pdf)
http://downloads.hindawi.com/journals/DDNS/2016/6516258.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:jnddns:6516258

DOI: 10.1155/2016/6516258

Access Statistics for this article

More articles in Discrete Dynamics in Nature and Society from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().