Hidden tree Markov models allow learning distributions for tree structured data while being interpretable as nondeterministic automata. We provide a concise summary of the main approaches in literature, focusing in particular on the causality assumptions introduced by the choice of a specific tree visit direction. We will then sketch a novel non-parametric generalization of the bottom-up hidden tree Markov model with its interpretation as a nondeterministic tree automaton with infinite states.
|Titolo:||Learning Tree Distributions by Hidden Markov Models|
|Anno del prodotto:||2018|
|Appare nelle tipologie:||4.2 Abstract in Atti di convegno|