An iterative precision vector to optimise the CBR adaptation of EquiVox - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Engineering Applications of Artificial Intelligence Année : 2014

An iterative precision vector to optimise the CBR adaptation of EquiVox

Résumé

The case-based reasoning (CBR) approach consists in retrieving solutions from similar past problems and adapting them to new ones. Interpolation tools can easily be used as adaptation tools in CBR systems. The accuracies of interpolated results depend on the set of known solved problems with which the interpolation tools have previously been trained. To be sufficiently accurate, an interpolation tool must be trained with a large number of known cases. However, CBR systems are also relevant if the number of known cases is restricted. In addition, the training of interpolation tools is generally seen by users as a black box. This paper presents a generic method to optimise CBR adaptations driven by trained interpolation tools and also takes into account remarks made by users about known solution accuracy. This method was applied to the CBR system called EquiVox which retrieves, reuses (interpolates), revises and retains three-dimensional numerical representations of organ contours and thus enhances its own performance.
Fichier non déposé

Dates et versions

hal-01011064 , version 1 (22-06-2014)

Identifiants

Citer

Julien Henriet, Pascal Chatonnay, Pierre-Emmanuel Leni. An iterative precision vector to optimise the CBR adaptation of EquiVox. Engineering Applications of Artificial Intelligence, 2014, EAAI2146 (35), pp.158-163. ⟨10.1016/j.engappai.2014.06.017⟩. ⟨hal-01011064⟩
153 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More