Prediction of individual sequences using universal deterministic finite state machines

Amir Ingber, Meir Feder

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We consider the problem of universal prediction of individual binary sequences where the universal predictor is a deterministic finite state machine with a fixed number of states. We examine the case of self-information loss, where the predictions are probability assignments. The performance of the predictors is measured by their redundancy w.r.t. the constant predictors class. We obtain an improved lower bound on the redundancy of any finite state (FS) predictor with K states. We construct a FS predictor based on the lower bound and compare the performance of the predictor to the lower bound. Numerical results show that the redundancy of the proposed FS predictor is close to that predicted by the lower bound.

Original languageEnglish
Title of host publicationProceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006
Pages421-425
Number of pages5
DOIs
StatePublished - 2006
Event2006 IEEE International Symposium on Information Theory, ISIT 2006 - Seattle, WA, United States
Duration: 9 Jul 200614 Jul 2006

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8101

Conference

Conference2006 IEEE International Symposium on Information Theory, ISIT 2006
Country/TerritoryUnited States
CitySeattle, WA
Period9/07/0614/07/06

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