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Combinaisons d'automates et de boules de mots pour la classification de séquences

Frédéric Tantini 1, 2 Alain Terlutte 3 Fabien Torre 3, 4 
2 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
4 MOSTRARE - Modeling Tree Structures, Machine Learning, and Information Extraction
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : In this paper, we present a general framework for supervised classification. This framework only needs the definition of a generalisation operator and provides ensemble methods. For sequence classification tasks, we show that grammatical inference has already defined such learners for automata classes like reversible automata or k-TSS automata. Then we propose a generalisation operator for the class of balls of words. Finally, we show through experiments that our method efficiently resolves sequence classification tasks.
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Submitted on : Monday, November 21, 2011 - 9:24:38 AM
Last modification on : Saturday, June 25, 2022 - 7:44:29 PM
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Frédéric Tantini, Alain Terlutte, Fabien Torre. Combinaisons d'automates et de boules de mots pour la classification de séquences. Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle, 2011, Apprentissage artificiel, 25 (3), pp.411-434. ⟨10.3166/ria.25.411-434⟩. ⟨hal-00643057⟩



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