Regularization networks with indefinite kernels
type: Item
instance of: scholarly article
title: Regularization networks with indefinite kernels
zbMATH Open document ID: 1295.68141
author: Qiang Wu
published in: Journal of Approximation Theory
publication date: 2013-02-19T00:00:00Z
review text: The author considers the least squares regression problem by coefficient-based regularization schemes with \(\ell_2\) norm square as the penalty generated by indefinite kernels. The key interesting idea is to introduce a pair of reproducing kernel Hilbert spaces for a singular value decomposition of the kernel integral. By methods from functional analysis, the author derives capacity independent error bounds and learning rates.
reviewed by: Ding-Xuan Zhou
Mathematics Subject Classification ID: 68Q32
Mathematics Subject Classification ID: 62J02
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 46E22
zbMATH DE Number: 6137304
zbMATH Keywords: learning theory
zbMATH Keywords: coefficient-based regularization
zbMATH Keywords: indefinite kernel
zbMATH Keywords: least square regression
zbMATH Keywords: reproducing kernel Hilbert space
zbMATH Keywords: capacity independent error bound
zbMATH Keywords: learning rates
MaRDI profile type: Publication
full work available at URL: j.jat.2012.10.001
OpenAlex ID: W1982891175
DOI: 10.1016/J.JAT.2012.10.001
description: scientific article; zbMATH DE number 6137304