Indefinite kernel network with \(l^q\)-norm regularization
type: Item
instance of: scholarly article
title: Indefinite kernel network with \(l^q\)-norm regularization
zbMATH Open document ID: 1417.62093
DOI: 10.1155/2016/6516258
author: Zhongfeng Qu
author: Hongwei Sun
published in: Discrete Dynamics in Nature and Society
publication date: 2019-02-19T00:00:00Z
review text: Summary: We study the asymptotical properties of indefinite kernel network with \(l^q\)-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 \(\mathcal{X}\) and \(L_\tau\) condition for the probability measure \(\rho_{\mathcal{X}}\), satisfied error bounds and learning rates are deduced.
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62G20
zbMATH DE Number: 7025641
Wikidata QID: Q59123613
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/6516258
OpenAlex ID: W2324680782
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description: scientific article; zbMATH DE number 7025641