Dissertation/Thesis Abstract

Combining the normal hedge algorithm with weighted trees for predicting binary sequences
by Biaggi, Andrea, M.S., University of California, San Diego, 2010, 41; 1474775
Abstract (Summary)

This thesis presents the use of an online learning hedging technique to predict patterns in a binary sequence. It is compared to previous techniques. This technique, referenced as Normal Hedge Tree, has faster learning rates for patterns and suffers less regret with respect to Hedge(η) [FS99, FS95] with synthetically generated sequences. Normal Hedge Tree is compared to Mindreader [Dos] over previously collected sequences. Overall, Normal Hedge Tree performs worse than Mindreader but for some sequences it has better results. We combine the two algorithms using Normal Hedge [CFH09], but the combination performs worse than either of the algorithms taken singularly.

Indexing (document details)
Advisor: Freund, Yoav
Commitee: Dasgupta, Sanjoy, Elkan, Charles
School: University of California, San Diego
Department: Computer Science and Engineering
School Location: United States -- California
Source: MAI 48/04M, Masters Abstracts International
Subjects: Artificial intelligence, Computer science
Keywords: Binary sequences, Hedging, Normal hedge, Prediction
Publication Number: 1474775
ISBN: 978-1-109-70417-4
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