Concentration estimates for learning with unbounded sampling
type: Item
instance of: scholarly article
title: Concentration estimates for learning with unbounded sampling
zbMATH Open document ID: 1283.68289
author: Ding-Xuan Zhou
author: Zheng-Chu Guo
published in: Advances in Computational Mathematics
publication date: 2013-04-15T00:00:00Z
review text: Kernel based least square regression learning with unbounded sampling processes is studied. By a moment hypothesis for unbounded sampling, an approximation assumption for the regression function, and capacity condition for hypothesis space, sharper learning rates are derived. In the error analysis, a probability inequality for unbounded random variables is introduced, and an iteration technique is used.
reviewed by: Hongwei Sun
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62J02
zbMATH DE Number: 6153873
zbMATH Keywords: learning theory
zbMATH Keywords: least-square regression
zbMATH Keywords: regularization in reproducing kernel Hilbert spaces
zbMATH Keywords: empirical covering number
zbMATH Keywords: concentration estimates
MaRDI profile type: Publication
full work available at URL: s10444-011-9238-8
OpenAlex ID: W2006650550
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DOI: 10.1007/S10444-011-9238-8
description: scientific article; zbMATH DE number 6153873