Logical settings for concept learning from incomplete examples in First Order Logic

Autor: Bouthinon, Dominique, Soldano, Henry, Ventos, Véronique

We investigate here concept learning from incomplete examples. Our first purpose is to discuss to what extent logical learning settings have to be modified in order to cope with data incompleteness. More precisely we are interested in extending the l

Externí odkaz: http://arxiv.org/abs/cs/0607096

Relational Learning from Ambiguous Examples

Autor: Bouthinon, Dominique, Soldano, Henry

Dans cet article nous étudions une situation d'apprentissage relationnel où chaque exemple est ambigu dans le sens où il est dissimulé au sein d'un ensemble de descriptions. Ce cas apparait en apprentissage de règles lorsque les valeurs de véri

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______166::0997480c7998dd01e0e905a305f0310a
https://hal.archives-ouvertes.fr/hal-00989218

Abstract and Local Rule Learning in Attributed Networks.

Autor: Soldano, Henry, Santini, Guillaume, Bouthinon, Dominique

Publikováno v: Foundations of Intelligent Systems: 22nd International Symposium, ISMIS 2015, Lyon, France, October 21-23, 2015, Proceedings; 2015, p313-323, 11p

Local Knowledge Discovery in Attributed Graphs.

Autor: Soldano, Henry, Santini, Guillaume, Bouthinon, Dominique

Publikováno v: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI); 2015, p250-257, 8p

Learning First Order Rules from Ambiguous Examples.

Autor: Bouthinon, Dominique, Soldano, Henry

Publikováno v: 2014 IEEE 26th International Conference on Tools with Artificial Intelligence; 2014, p39-46, 8p