Learning Prediction Suffix Trees with Winnow

author: Nikos Karampatziakis, Department of Computer Science, Cornell University
published: Aug. 26, 2009,   recorded: June 2009,   views: 4737


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Prediction suffix trees (PSTs) are a popular tool for modeling sequences and have been successfully applied in many domains such as compression and language modeling. In this work we adapt the well studied Winnow algorithm to the task of learning PSTs. The proposed algorithm automatically grows the tree, so that it provably remains competitive with any fixed PST determined in hindsight. At the same time we prove that the depth of the tree grows only logarithmically with the number of mistakes made by the algorithm. Finally, we empirically demonstrate its effectiveness in two different tasks.

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