Learning from Partially Labeled Data

Siamak Mehrkanoon, Xiaolin Huang, Johan A. K. Suykens

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

Providing sufficient labeled training data in many application domains is a laborious and costly task. Designing models that can learn from partially labeled data, or leveraging labeled data in one domain and unlabeled data in a different but related domain is of great interest in many applications. In particular, in this context one can refer to semi-supervised modelling, transfer learning, domain adaptation and multi-view learning among oth- ers. There are several possibilities for designing such models ranging from shallow to deep models. These type of models have received increasing in- terest due to their successful applications in real-life problems. This paper provides a brief overview of recent techniques in learning from partially labeled data.

Original languageEnglish
Title of host publicationESANN 2020 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Pages493-502
Number of pages10
ISBN (Electronic)9782875870742
Publication statusPublished - 2020
EventEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Online, Brugge, Belgium
Duration: 2 Oct 20204 Oct 2020
Conference number: 28
https://www.esann.org/esann20programme

Symposium

SymposiumEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Abbreviated titleESANN 2020
Country/TerritoryBelgium
CityBrugge
Period2/10/204/10/20
Internet address

Fingerprint

Dive into the research topics of 'Learning from Partially Labeled Data'. Together they form a unique fingerprint.

Cite this