Discrete sequence prediction and its applications (1994)
| Venue: | Machine Learning |
| Citations: | 30 - 2 self |
BibTeX
@INPROCEEDINGS{Laird94discretesequence,
author = {Philip Laird and Ronald Saul and Philip Laird and Ronald Saul},
title = {Discrete sequence prediction and its applications},
booktitle = {Machine Learning},
year = {1994},
pages = {43--68}
}
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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 practi-ca] 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: dynamica]ly optimizing Prolog programs for good average-case behavior and maintaining a cache for a database on mass storage.
