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Contributions aux communications inter-vues pour l'apprentissage collaboratif

Abstract : This thesis presents several methods to optimize and improve inter-views communications in a collaborative learning context: The first contribution is about the improvement of communications for Collaborative Clustering using a learning method making it possible for a view to weight the information supplied by the external views. This methods is based on the resolution of a problem made of the Collaborative Clustering criterion with two constraints of the weighting coefficients. A second contribution consists in the definition of an incremental learning method of Self-Organizing Maps, followed by its adaptation to Collaborative Clustering. This method makes it possible to adapt the results obtained using Collaborative Clustering in case of a potential evolution in data distribution through time. The second axis consists in the definition of a new paradigm, called Collaborative Reconstruction. In this paradigm, several views collaborate to reconstruct local missing data. This method is based on neural networks linking external data and local data. The combination of the external informations is guaranteed by a weighting method favoring the best reconstructed features for each external view.
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Submitted on : Tuesday, May 19, 2020 - 6:41:43 AM
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  • HAL Id : tel-02612226, version 1


Denis Maurel. Contributions aux communications inter-vues pour l'apprentissage collaboratif. Machine Learning [cs.LG]. Sorbonne Université, 2018. English. ⟨NNT : 2018SORUS489⟩. ⟨tel-02612226⟩



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