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Introduction
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Time Varying Active Appearance
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Conclusion
Index
Variable length Markov model
Subsections
Introduction
Encoding transition probabilities
The storage of transitions
Towards a more effective storage of transitions
The learning
Definitions
The learning algorithm
General idea
The pruning of nodes
The algorithm
The estimation of observed probabilities
Laplace's law of succession
The maximum likelihood estimate
Lidstone's law of succession
The natural law of succession
Comparison of the probability distributions
The Kullback-Leibler divergence
The Matusita distance
Prediction using VLMM
Conclusion
franck 2006-10-16
