Effects of drift and noise on the optimal sliding window size for data stream regression models

DOI10.1080/03610926.2015.1096388zbMath1409.68242OpenAlexW2408360949MaRDI QIDQ5351746

Katharina Tschumitschew, Frank Klawonn

Publication date: 30 August 2017

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10033/621230

zbMATH Keywords

online learningconcept driftadaptive regressiondata stream analysis

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Sums of independent random variables; random walks (60G50) Learning and adaptive systems in artificial intelligence (68T05) Online algorithms; streaming algorithms (68W27)

This page was built for publication: Effects of drift and noise on the optimal sliding window size for data stream regression models