Improved Smoothing for Probabilistic Suffix Trees Seen as Variable Order Markov Chains

Authors:
Christopher Kermorvant;Pierre Dupont
Affiliations:
-;-
Venue:
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Year:
2002

Citing 2
Cited 2

Approximate statistical tests for comparing supervised classification learning algorithms

Neural Computation
Protein Family Classification Using Sparse Markov Transducers

Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology

Prediction suffix trees for supervised classification of sequences

Pattern Recognition Letters
On prediction using variable order Markov models

Journal of Artificial Intelligence Research

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Abstract

In this paper, we compare Probabilistic Suffix Trees (PST), recently proposed, to a specific smoothing of Markov chains and show that they both induce the same model, namely a variable order Markov chain. We show a weakness of PST in terms of smoothing and propose to use an enhanced smoothing. We show that the model based on enhanced smoothing outperform the PST while needing less parameters on a protein domain detection task on public databases.