Adaptive Online Prediction Using Weighted Windows

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抄録

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.

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.

収録刊行物

  • IEICE transactions on information and systems

    IEICE transactions on information and systems 94(10), 1917-1923, 2011-10-01

    The Institute of Electronics, Information and Communication Engineers

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各種コード

  • NII論文ID(NAID)
    10030193290
  • NII書誌ID(NCID)
    AA10826272
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    09168532
  • データ提供元
    CJP書誌  IR  J-STAGE 
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