Online Equivalence Learning Through A Quasi-Newton Method - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2012

Online Equivalence Learning Through A Quasi-Newton Method

Résumé

Recently, the community has shown a growing interest in building online learning models. In this paper, we are interested in the framework of fuzzy equivalences obtained by residual implications. Models are generally based on the relevance degree between pairs of objects of the learning set, and the update is obtained by using a standard stochastic (online) gradient descent. This paper proposes another method for learning fuzzy equivalences using a Quasi-Newton optimization. The two methods are extensively compared on real data sets for the task of nearest sample(s) classification.
Fichier principal
Vignette du fichier
CR-fzzOEL.pdf (225.64 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00685467 , version 1 (04-10-2012)

Identifiants

  • HAL Id : hal-00685467 , version 1

Citer

Hoel Le Capitaine. Online Equivalence Learning Through A Quasi-Newton Method. IEEE Int. Conf. on Fuzzy Systems, Jun 2012, Brisbane, Australia. pp.1-8. ⟨hal-00685467⟩
128 Consultations
110 Téléchargements

Partager

Gmail Facebook X LinkedIn More