Learning Parameters for a Knowledge Diagnostic Tools in Orthopedic Surgery

Sébastien Lalle 1, 2 Vanda Luengo 3
1 SILEX - Supporting Interaction and Learning by Experience
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
3 MeTAH
LIG - Laboratoire d'Informatique de Grenoble
Abstract : We provide and illustrate a methodology for taking into account data for a knowledge diagnosis method in orthopaedical surgery, using Bayesian networks and machine learning techniques. We aim to make the conception of the student model less time-consuming and subjective. A first Bayesian network was built like an expert system, where experts (in didactic and surgery) provide both the structure and the probabilities. However, learning the probability distributions of the variables allows going from an expert network toward a more data-centric one. We compare and analyze here various learning algorithms with regard to experimental data. Then we point out some crucial issues like the lack of data.
Type de document :
Communication dans un congrès
Mykola Pechenizkiy, Toon Calders, Cristina Conati, Sebastián Ventura, Cristóbal Romero, John C. Stamper. EDM 2011 - International Conference on Educational Data Mining, Jul 2011, Eindhoven, Netherlands. pp.369-370, 2011
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https://hal.archives-ouvertes.fr/hal-00911383
Contributeur : Denis Bouhineau <>
Soumis le : lundi 2 décembre 2013 - 15:38:05
Dernière modification le : mercredi 31 octobre 2018 - 12:24:23

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  • HAL Id : hal-00911383, version 1

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Sébastien Lalle, Vanda Luengo. Learning Parameters for a Knowledge Diagnostic Tools in Orthopedic Surgery. Mykola Pechenizkiy, Toon Calders, Cristina Conati, Sebastián Ventura, Cristóbal Romero, John C. Stamper. EDM 2011 - International Conference on Educational Data Mining, Jul 2011, Eindhoven, Netherlands. pp.369-370, 2011. 〈hal-00911383〉

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