Modèles Relationnels Probabilistes et Incertitude de Références : Apprentissage de structure avec algorithmes de partitionnement

Anthony Coutant 1
1 DUKe - Data User Knowledge
LINA - Laboratoire d'Informatique de Nantes Atlantique : UMR 6241
Abstract : We are surrounded by heterogeneous and interdependent data. The i.i.d. assumption has shown its limits in the algorithms considering tabular datasets, containing individuals with same data domain and without mutual influence on each other. Statistical relational learning aims at representing knowledge, reasoning, and learning in multi-relational datasets with uncertainty and lifted probabilistic graphical models offer a solution for generative learning in this context. We study in this thesis a type of directed lifted graphical model, called probabilistic relational models, in the context of reference uncertainty, i.e. where dataset’s individuals can have uncertainty over both their internal attributes description and their external memberships in associations with others, having the particularity of relying on individuals partitioning functions in order to find out general knowledge. We show existing models’ limits for learning in this context and propose extensions allowing to use relational clustering methods, more adequate for the problem, and offering a less constrained representation bias permitting extra knowledge discovery, especially between associations types in the relational data domain.
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Anthony Coutant. Modèles Relationnels Probabilistes et Incertitude de Références : Apprentissage de structure avec algorithmes de partitionnement. Apprentissage [cs.LG]. Université de Nantes, 2015. Français. ⟨tel-01254524⟩

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