Sequences Classification by Least General Generalisations
Résumé
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.
Domaines
Apprentissage [cs.LG]Origine | Fichiers produits par l'(les) auteur(s) |
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