Mixture of Hidden Markov Models as Tree Encoder

Abstract

The paper introduces a new probabilistic tree encoder based on a mixture of Bottom-up Hidden Tree Markov Models. The ability to recognise similar structures in data is experimentally assessed both in clusterization and classication tasks. The results of these preliminary experiments suggest that the model can be successfully used to compress the tree structural and label patterns in a vectorial representation.

Publication
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Date
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