DocumentCode
3116152
Title
Map Model Selection for Context Trees
Author
Tjalkens, Tjalling ; Willems, Frans
Author_Institution
Dept. of Electr. Eng., Eindhoven Univ. of Technol., Eindhoven
fYear
2006
fDate
6-8 Sept. 2006
Firstpage
129
Lastpage
134
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.
Keywords
Markov processes; decision trees; maximum likelihood estimation; probability; Markov model; context tree model; decision tree; maximum a-posteriori probability model; natural language modelling; Binary sequences; Binary trees; Classification algorithms; Classification tree analysis; Context modeling; Data compression; Decision trees; Maximum a posteriori estimation; Natural languages; Random variables;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on
Conference_Location
Arlington, VA
ISSN
1551-2541
Print_ISBN
1-4244-0656-0
Electronic_ISBN
1551-2541
Type
conf
DOI
10.1109/MLSP.2006.275535
Filename
4053634
Link To Document