Adaptive online prediction using weighted windows

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

We propose online prediction algorithms for data streams whose characteristics might change over time. Our algorithms are applications of online learning with experts. In particular, our algorithms combine base predictors over sliding windows with different length as experts. As a result, our algorithms are guaranteed to be competitive with the base predictor with the best fixed-length sliding window in hindsight.

Original languageEnglish
Pages (from-to)1917-1923
Number of pages7
JournalIEICE Transactions on Information and Systems
VolumeE94-D
Issue number10
DOIs
Publication statusPublished - Oct 2011

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

Cite this

Adaptive online prediction using weighted windows. / Yoshida, Shin Ichi; Hatano, Kohei; Takimoto, Eiji; Takeda, Masayuki.

In: IEICE Transactions on Information and Systems, Vol. E94-D, No. 10, 10.2011, p. 1917-1923.

Research output: Contribution to journalArticle

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