Towards Uncovering the True Use of Unlabeled Data in Machine Learning

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.

Grantor dc:publisher

Università degli studi di Trento

Year dc:date

2018

Author dc:creator

• Sansone, Emanuele

Contributors dc:contributor

• De Natale, Francesco

• Settore INF/01 - Informatica
• Settore ING-INF/03 - Telecomunicazioni
• Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni

Statement dc:rights

• info:eu-repo/semantics/openAccess
• license:Tutti i diritti riservati (All rights reserved)
• license uri:iris.PRI01

Language dc:language

eng

Identifier

http://dx.doi.org/10.15168/11572_367731
10.15168/11572_367731

OAI identifier oai:identifier

oai:iris.unitn.it:11572/367731