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Communication Dans Un Congrès Année : 2024

Classifying Words with 3-sort Automata

Résumé

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.
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Dates et versions

hal-04568939 , version 1 (06-05-2024)

Identifiants

Citer

Tomasz Jastrząb, Frédéric Lardeux, Éric Monfroy. Classifying Words with 3-sort Automata. 16th International Conference on Agents and Artificial Intelligence, Feb 2024, Rome, France. pp.1179-1188, ⟨10.5220/0012454100003636⟩. ⟨hal-04568939⟩

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