Abstract : In this paper, we present a general framework for supervised classification. This framework provides methods like boosting and only needs the definition of a generalisation operator called LGG. For sequence classification tasks, LGG is a learner that only uses positive examples. We show that grammatical inference has already defined such learners for automata classes like reversible automata ork-TSS automata. Then we propose a generalisation algorithm for the class of balls of words. Finally, we show through experiments that our method efficiently resolves sequence classification tasks.
https://hal.inria.fr/inria-00524707
Contributeur : Fabien Torre
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Soumis le : vendredi 8 octobre 2010 - 15:55:26
Dernière modification le : mercredi 25 juillet 2018 - 14:05:30
Document(s) archivé(s) le : jeudi 25 octobre 2012 - 16:45:17
Frédéric Tantini, Alain Terlutte, Fabien Torre. Sequences Classification by Least General Generalisations. José M. Sempere and Pedro Garcia. 10th International Colloquium on Grammatical Inference, Sep 2010, Valencia, Spain. Springer, 6339, pp.189-202, 2010, Lecture Notes in Artificial Intelligence. 〈http://www.springerlink.com/content/e6270247p5048l21/〉. 〈10.1007/978-3-642-15488-1_16〉. 〈inria-00524707〉
