Discrete Sequence Prediction and Its Applications

Authors:
Philip Laird;Ronald Saul
Affiliations:
AI Research Branch, NASA Ames Research Center, Moffett Field, California 94035-1000. LAIRD@PLUTO.ARC.NASA.GOV;Recom Technologies, Inc., NASA Ames Research Center, Moffett Field, California 94035-1000. SAUL@KRONOS.ARC.NASA.GOV
Venue:
Machine Learning
Year:
1994

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Abstract

Learning from experience to predict sequences of discrete symbols is a fundamental problem in machine learning with many applications. We present a simple and practical algorithm (TDAG) for discrete sequence prediction. Based on a text-compression method, the TDAG algorithm limits the growth of storage by retaining the most likely prediction contexts and discarding (forgetting) less likely ones. The storage/speed tradeoffs are parameterized so that the algorithm can be used in a variety of applications. Our experiments verify its performance on data compression tasks and show how it applies to two problems: dynamically optimizing Prolog programs for good average-case behavior and maintaining a cache for a database on mass storage.