Learning bounds of ERM principle for sequences of time-dependent samples
type: Item
instance of: scholarly article
title: Learning bounds of ERM principle for sequences of time-dependent samples
zbMATH Open document ID: 1464.62339
DOI: 10.1155/2015/826812
author: Mingchen Yao
author: Chao Zhang
author: Wei Wu
published in: Discrete Dynamics in Nature and Society
publication date: 2019-02-19T00:00:00Z
review text: Summary: Many generalization results in learning theory are established under the assumption that samples are independent and identically distributed (i.i.d.). However, numerous learning tasks in practical applications involve the time-dependent data. In this paper, we propose a theoretical framework to analyze the generalization performance of the empirical risk minimization (ERM) principle for sequences of time-dependent samples (TDS). In particular, we first present the generalization bound of ERM principle for TDS. By introducing some auxiliary quantities, we also give a further analysis of the generalization properties and the asymptotical behaviors of ERM principle for TDS.
Mathematics Subject Classification ID: 62J02
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62M99
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 7025510
Wikidata QID: Q59105483
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/826812
OpenAlex ID: W2177289819
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description: scientific article; zbMATH DE number 7025510