Sequences Classification by Least General Generalisations - Archive ouverte HAL Access content directly
Conference Papers Year : 2010

Sequences Classification by Least General Generalisations

(1, 2) , (3) , (3, 4)
1
2
3
4

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.
Fichier principal
Vignette du fichier
GloBallICGI2010.pdf (401.1 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

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

Identifiers

Cite

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⟩
473 View
285 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More