Accéder directement au contenu Accéder directement à la navigation
Communication dans un congrès

Dispersion effect on generalisation error in classification: Experimental proof and practical algorithm

Abstract : Recent theoretical work proposes criteria of dispersion to generate learning points. The aim of this paper is to convince the reader, with experimental proofs, that dispersion is a good criterion in practice for generating learning points for classification problems. Problem of generating learning points consists then in generating points with the lowest dispersion. As a consequence, we present low dispersion algorithms existing in the literature, analyze them and propose a new algorithm.
Type de document :
Communication dans un congrès
Liste complète des métadonnées

https://hal.inrae.fr/hal-02596424
Déposant : Migration Irstea Publications <>
Soumis le : vendredi 15 mai 2020 - 21:00:31
Dernière modification le : jeudi 8 octobre 2020 - 17:06:02

Identifiants

  • HAL Id : hal-02596424, version 1
  • IRSTEA : PUB00034342

Collections

Citation

Benoît Gandar, G. Loosli, Guillaume Deffuant. Dispersion effect on generalisation error in classification: Experimental proof and practical algorithm. ICAART 2011 Conference .3rd Congference on Agents and Artificial Intelligence, Jan 2011, Rome, Italy. pp.4. ⟨hal-02596424⟩

Partager

Métriques

Consultations de la notice

15