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Adaptive Online Prediction Using Weighted Windows
Shin-ichi YOSHIDA Kohei HATANO Eiji TAKIMOTO Masayuki TAKEDA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2011/10/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Information-Based Induction Sciences and Machine Learning)
machine learning, data stream, online learning, sliding window,
Full Text: PDF(526.6KB)
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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.