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 |
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| Title of host publication | ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
| Publisher | ESANN (i6doc.com) |
| Pages | 493-502 |
| Number of pages | 10 |
| ISBN (Electronic) | 9782875870742 |
| Publication status | Published - 2020 |
| Event | 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020 - Virtual, Online, Belgium Duration: 2 Oct 2020 → 4 Oct 2020 |
Publication series
| Name | ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
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Conference
| Conference | 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020 |
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| Country/Territory | Belgium |
| City | Virtual, Online |
| Period | 2/10/20 → 4/10/20 |
Bibliographical 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 Unionâs Horizon 2020 research and innovation program / ERC Advanced Grant E-DUALITY (787960). This paper reflects only the authorsâ 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:
© ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
Funding
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 Unionâs Horizon 2020 research and innovation program / ERC Advanced Grant E-DUALITY (787960). This paper reflects only the authorsâ 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.