Learning from Partially Labeled Data

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

Learning from Partially Labeled Data

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

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-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 language	English
Title of host publication	ESANN 2020 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Pages	493-502
Number of pages	10
ISBN (Electronic)	9782875870742
Publication status	Published - 2020
Event	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Online, Brugge, Belgium Duration: 2 Oct 2020 → 4 Oct 2020 Conference number: 28 https://www.esann.org/esann20programme

Symposium

Symposium	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Abbreviated title	ESANN 2020
Country/Territory	Belgium
City	Brugge
Period	2/10/20 → 4/10/20
Internet address	https://www.esann.org/esann20programme

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Cite this

@inproceedings{6f070672bf734b90af9d2f76be235756,

title = "Learning from Partially Labeled Data",

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.",

author = "Siamak Mehrkanoon and Xiaolin Huang and Suykens, {Johan A. K.}",

note = "Funding Information: This work was supported by the Postdoctoral Fellowship of the Research Foundation-Flanders (FWO: 12Z1318N). National Natural Science Foundation of China (No. 61977046). EU: The research leading to these results has received funding from the European Research Council under the European Union{\^a}s Horizon 2020 research and innovation program / ERC Advanced Grant E-DUALITY (787960). This paper reflects only the authors{\^a} views and the Union is not liable for any use that may be made of the contained information.Research Council KUL: Optimization frameworks for deep kernel machines C14/18/068. Flemish Government: FWO: projects: GOA4917N (Deep Restricted Kernel Machines: Methods and Foundations), PhD/Postdoc grant. Impulsfonds AI: VR 2019 2203 DOC.0318/1QUATERKenniscentrum Data en Maatschappij. Ford KU Leuven Research Alliance Project KUL0076. Siamak Mehrkanoon is an assistant professor at Maastricht University. Xiaolin Huang is an associate professor at Shanghai Jiao Tong University. Johan Suykens is a full professor at the KU Leuven, Belgium. Publisher Copyright: {\textcopyright} ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.; European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020 ; Conference date: 02-10-2020 Through 04-10-2020",

year = "2020",

language = "English",

pages = "493--502",

booktitle = "ESANN 2020 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

url = "https://www.esann.org/esann20programme",

}

Mehrkanoon, S, Huang, X & Suykens, JAK 2020, Learning from Partially Labeled Data. in ESANN 2020 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 493-502, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Brugge, Belgium, 2/10/20. <https://www.esann.org/sites/default/files/proceedings/2020/ES2020-1.pdf>

TY - GEN

T1 - Learning from Partially Labeled Data

AU - Mehrkanoon, Siamak

AU - Huang, Xiaolin

AU - Suykens, Johan A. K.

N1 - Conference code: 28

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

M3 - Conference article in proceeding

SP - 493

EP - 502

BT - ESANN 2020 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

T2 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Y2 - 2 October 2020 through 4 October 2020

ER -

Learning from Partially Labeled Data

Abstract

Symposium

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