MAP model selection for context trees

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Abstract

Context tree models are Markov models where the conditioning is a string of previous symbols of variable length. These models are applicable for the modelling of natural languages and computer data. Also a decision tree can be seen as a context tree model. In this paper we derive an efficient method to determine the Maximum A-posteriori Probability model from a large set of context trees.
Original languageEnglish
Title of host publicationProc. of the 2006 IEEE Signal Processing Workshop
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages129-134
Number of pages6
ISBN (Print)1-4244-0656-0
DOIs
Publication statusPublished - 2006
Event16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing (MLSP 2006) - Arlington, United States
Duration: 6 Sep 20068 Sep 2006
Conference number: 16
http://mlsp2006.conwiz.dk/

Conference

Conference16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing (MLSP 2006)
Abbreviated titleMLSP 2006
CountryUnited States
CityArlington
Period6/09/068/09/06
Internet address

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