Online Sequential Prediction via Incremental Parsing: The Active LeZi Algorithm
- 29 January 2007
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Intelligent Systems
- Vol. 22 (1), 52-58
- https://doi.org/10.1109/mis.2007.15
Abstract
Intelligent systems that can predict future events can make more reliable decisions. Active LeZi, a sequential prediction algorithm, can reason about the future in stochastic domains without domain-specific knowledge. In this article, potential of constructing a prediction algorithm based on data compression techniques are investigated. Active LeZi prediction algorithm approaches sequential prediction from an information-theoretic standpoint. For any sequence of events that can be modeled as a stochastic process, ALZ uses Markov models to optimally predict the next symbolKeywords
This publication has 7 references indexed in Scilit:
- Context allocation with application to data compressionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- The role of prediction algorithms in the MavHome smart home architectureIEEE Wireless Communications, 2002
- Unbounded length contexts for PPMPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- LeZi-Update: An Information-Theoretic Framework for Personal Mobility Tracking in PCS NetworksWireless Networks, 2002
- Discrete sequence prediction and its applicationsMachine Learning, 1994
- Universal prediction of individual sequencesIEEE Transactions on Information Theory, 1992
- Compression of individual sequences via variable-rate codingIEEE Transactions on Information Theory, 1978