Adaptive Online Prediction Using Weighted Windows

Shin-ichi YOSHIDA  Kohei HATANO  Eiji TAKIMOTO  Masayuki TAKEDA  

IEICE TRANSACTIONS on Information and Systems   Vol.E94-D   No.10   pp.1917-1923
Publication Date: 2011/10/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E94.D.1917
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)>>
Buy this Article

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.