2026-09-19T01:54:55Zhttps://iris.unitn.it/oai/requestoai:iris.unitn.it:11572/3677312026-04-03T00:48:41Zcom_11572_237821com_11572_101871col_11572_237822ou_ou00002
Towards Uncovering the True Use of Unlabeled Data in Machine Learning
Sansone, Emanuele
Sansone, Emanuele
De Natale, Francesco
Settore INF/01 - Informatica
Settore ING-INF/03 - Telecomunicazioni
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
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.
2018
info:eu-repo/semantics/doctoralThesis
https://hdl.handle.net/11572/367731
http://dx.doi.org/10.15168/11572_367731
10.15168/11572_367731
eng
firstpage:1
lastpage:86
numberofpages:86
info:eu-repo/semantics/openAccess
Università degli studi di Trento
place:TRENTO
license:Tutti i diritti riservati (All rights reserved)
license:Tutti i diritti riservati (All rights reserved)
license uri:iris.PRI01