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 classification 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.
Mixture of Hidden Markov Models as Tree Encoder / Davide Bacciu; CASTELLANA, DANIELE. - STAMPA. - (2018), pp. 543-548. (Intervento presentato al convegno European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'18) tenutosi a Bruges, Belgium nel 25-27 Aprile 2018).
Mixture of Hidden Markov Models as Tree Encoder
CASTELLANA, DANIELE
2018
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 classification 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.
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