Towards Uncovering the True Use of Unlabeled Data in Machine Learning

Sansone, Emanuele (2018) Towards Uncovering the True Use of Unlabeled Data in Machine Learning. PhD thesis, University of Trento.

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Abstract

Knowing how to exploit unlabeled data is a fundamental problem in machine learning. This dissertation provides contributions in different contexts, including semi-supervised learning, positive unlabeled learning and representation learning. In particular, we ask (i) whether is possible to learn a classifier in the context of limited data, (ii) whether is possible to scale existing models for positive unlabeled learning, and (iii) whether is possible to train a deep generative model with a single minimization problem.

Item Type:Doctoral Thesis (PhD)
Doctoral School:Information and Communication Technology
PhD Cycle:29
Subjects:Area 01 - Scienze matematiche e informatiche > INF/01 INFORMATICA
Area 09 - Ingegneria industriale e dell'informazione > ING-INF/03 TELECOMUNICAZIONI
Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
Repository Staff approval on:30 Mar 2018 11:50

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