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, 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 |
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Abbreviated title | ESANN 2020 |
Country/Territory | Belgium |
City | Brugge |
Period | 2/10/20 → 4/10/20 |
Internet address |