Machine Learning

, Volume 15, Issue 1, pp 43–68

Discrete sequence prediction and its applications

  • Philip Laird
  • Ronald Saul


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.


sequence extrapolation statistical learning text compression speedup learning memory management 

Copyright information

© Kluwer Academic Publishers 1994

Authors and Affiliations

  • Philip Laird
    • 1
  • Ronald Saul
    • 2
  1. 1.AI Research BranchNASA Ames Research CenterMoffett Field
  2. 2.Recom Technologies, Inc.NASA Ames Research CenterMoffett Field

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