Input-Output Hidden Markov Models for trees
نویسندگان
چکیده
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.
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