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RNTI

MODULAD
Approche relationnelle de l'apprentissage de séquences
In EGC 2015, vol. RNTI-E-28, pp.481-482
Résumé
We observe an increasing amount of sequential data, for instance open data sources provide real-time information. In order to apply classical learning algorithms, sequential data are often modelled in an attribute-value setting using a sliding window. In this paper, we propose a relational approach. A first advantage is to let the relational algorithm choose the length of the window. A second advantage is to allow to consider conditions based on the existential quantifier and aggregates. A third advantage is to be able to consider several granularities at the same time.