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Adaptive context trees and text clustering

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1 Author(s)
J. -P. Vert ; Dept. of Math. & Applications, Ecole Normale Superieure, Paris, France

In the finite-alphabet context we propose four alternatives to fixed-order Markov models to estimate a conditional distribution. They consist in working with a large class of variable-length Markov models represented by context trees, and building an estimator of the conditional distribution with a risk of the same order as the risk of the best estimator for every model simultaneously, in a conditional Kullback-Leibler sense. Such estimators can be used to model complex objects like texts written in natural language and define a notion of similarity between them. This idea is illustrated by experimental results of unsupervised text clustering

Published in:

IEEE Transactions on Information Theory  (Volume:47 ,  Issue: 5 )