Adaptive Online Prediction Using Weighted Windows

• YOSHIDA Shin-ichi

  NTT West

• HATANO Kohei

  Department of Informatics, Kyushu University

• TAKIMOTO Eiji

  Department of Informatics, Kyushu University

• TAKEDA Masayuki

  Department of Informatics, Kyushu University

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.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E94-D (10), 1917-1923, 2011

  The Institute of Electronics, Information and Communication Engineers

Keywords

• machine learning
• data stream
• online learning
• sliding window

Details

• CRID: 1390001204378820736
• NII Article ID: 10030193290
• NII Book ID: AA10826272
• DOI: 10.1587/transinf.e94.d.1917
• ISSN: 17451361
• ISSN: 09168532
• HANDLE: 2324/25743
• HANDLE: 2324/1546622
• Web Site: http://www.jstage.jst.go.jp/article/transinf/E94.D/10/E94.D_10_1917/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • IRDB
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed