The paper introduces an input-driven generative model for tree-structured data that extends the bottom-up hidden tree Markov model with non-homogenous transition and emission probabilities. The advantage of introducing an input-driven dynamics in structured-data processing is experimentally investigated. The results of this preliminary analysis suggest that input-driven models can capture more discriminative structural information than non-input-driven approaches.
Input-Output Hidden Markov Models for Trees
SPERDUTI, ALESSANDRO
2012
Abstract
The paper introduces an input-driven generative model for tree-structured data that extends the bottom-up hidden tree Markov model with non-homogenous transition and emission probabilities. The advantage of introducing an input-driven dynamics in structured-data processing is experimentally investigated. The results of this preliminary analysis suggest that input-driven models can capture more discriminative structural information than non-input-driven approaches.
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
