On-line learning in changing environments with applications in supervised and unsupervised learning

Noboru Murata, Motoaki Kawanabe, Andreas Ziehe*, Klaus Robert Müller, Shun Ichi Amari

*この研究の対応する著者

研究成果: Article査読

43 被引用数 (Scopus)

抄録

An adaptive on-line algorithm extending the learning of learning idea is proposed and theoretically motivated. Relying only on gradient flow information it can be applied to learning continuous functions or distributions, even when no explicit loss function is given and the Hessian is not available. The framework is applied for unsupervised and supervised learning. Its efficiency is demonstrated for drifting and switching non-stationary blind separation tasks of acoustic signals. Furthermore applications to classification (US postal service data set) and time-series prediction in changing environments are presented.

本文言語English
ページ(範囲)743-760
ページ数18
ジャーナルNeural Networks
15
4-6
DOI
出版ステータスPublished - 2002 6

ASJC Scopus subject areas

  • 認知神経科学
  • 人工知能

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