Sequences Classification by Least General Generalisations - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2010

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.
Fichier principal
Vignette du fichier
GloBallICGI2010.pdf (401.1 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

inria-00524707 , version 1 (08-10-2010)

Identifiants

Citer

Frédéric Tantini, Alain Terlutte, Fabien Torre. Sequences Classification by Least General Generalisations. 10th International Colloquium on Grammatical Inference, Sep 2010, Valencia, Spain. pp.189-202, ⟨10.1007/978-3-642-15488-1_16⟩. ⟨inria-00524707⟩
485 Consultations
335 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More