An iterative precision vector to optimise the CBR adaptation of EquiVox

Abstract : 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.
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Article dans une revue
Engineering Applications of Artificial Intelligence, accept, Elsevier, 2014, EAAI2146 (35), pp.158-163. 〈10.1016/j.engappai.2014.06.017〉
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https://hal.inria.fr/hal-01011064
Contributeur : Julien Henriet <>
Soumis le : dimanche 22 juin 2014 - 21:22:53
Dernière modification le : jeudi 11 janvier 2018 - 06:22:18

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

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