Online Sequential Prediction via Incremental Parsing: The Active LeZi Algorithm

Authors:
Karthik Gopalratnam;Diane J. Cook
Affiliations:
University of Texas at Arlington;Washington State University
Venue:
IEEE Intelligent Systems
Year:
2007

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Abstract

Prediction is important in various domains. Intelligent systems that can predict future events can make more informed, and therefore more reliable, decisions. Active LeZi, an online sequential prediction algorithm that can reason about the future in stochastic domains, uses an information-theoretic approach to analyze synthetic data, UNIX command data, and sequential data obtained from the MavHome smart home environment.