Analyse théorique de l’apprentissage avec des fonctions de similarités pour l’adaptation de domaine

International audience ; Similarity learning is an active research area in machine learning that tackles the problem of finding a similarity function tailored to an observable data sample in order to achieve efficient classification. This learning scenario has been generally formalized by the means of a (, γ, τ)−good similarity learning framework in the context of supervised classification and has been shown to have important theoretical guarantees. In this paper , we propose to extend the theoretical analysis of similarity learning to the domain adaptation setting, a particular situation occurring when the similarity is learned and then deployed on samples following different probability distributions. We give a new definition of an (, γ)−good similarity for domain adaptation and prove several results quantifying the performance of a similarity function on a target domain after it has been trained on a source domain. We particularly show that if the source domain support contains that of the target then a notable improvement of the adaptation is achievable.