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VLMM results

This section presents the results we obtained in the evaluation of the variable length Markov model. All results have been obtained using texts as learning sequences, allowing quantitative assessment of the performance of the algorithms. The results are summarised in tables 6.2 and 6.3 (page ).

Subsections

Comparison of the Lidstone probability estimation with the maximum likelihood probability estimation
- Comparison of trees built using the Matusita distance
- Comparison of trees built using the Kullback-Leibler divergence
Comparison of trees built using the Matusita measure with trees built using the Kullback-Leibler divergence for large texts
Quantitative assessment of the prediction
- Performance given a large training set
- Performance given a small training set

franck 2006-10-01