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.
|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|
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