  Grammatical inference consists in learning a language or a grammar from data.
In this paper, we consider a number of models for inferring a non-deterministic
finite automaton (NFA) with 3 sorts of states, that must accept some words, and
reject some other words from a given sample. We then propose a transformation
from this 3-sort NFA into weighted-frequency and probabilistic NFA, and we
apply the latter to a classification task. The experimental evaluation of our
approach shows that the probabilistic NFAs can be successfully applied for
classification tasks on both real-life and superficial benchmark data sets.
