Thesis supervisor: László Aszalós
Location of studies (in Hungarian): University of Debrecen Doctoral School of Informatics Abbreviation of location of studies: ITDI
Description of the research topic:
In the majority of non-supervised learning algorithms, clustering is based on the distance between objects, however correlation clustering uses the similarity of objects. This allows us to introduce machine learning in new areas where earlier clustering methods did not allow for it. Our research focuses on the discovery of these areas and the construction of the tolerance correlation suitable for these cases. Emphasis is placed on solving various practical problems, taking into account the issue of efficiency.
Bibliography
[1] Bansal, Nikhil, Avrim Blum, and Shuchi Chawla. "Correlation clustering." Machine learning 56.1-3 (2004): 89-113.
[2] Kim, Sungwoong, et al. "Image segmentation using higher-order correlation clustering." IEEE transactions on pattern analysis and machine intelligence 36.9 (2014): 1761-1774.
[3] Samal, Mamata, V. Vijaya Saradhi, and Sukumar Nandi. "Scalability of correlation clustering." Pattern Analysis and Applications 21.3 (2018): 703-719.
[4] Keuper, Margret, et al. "Motion segmentation & multiple object tracking by correlation co-clustering." IEEE transactions on pattern analysis and machine intelligence (2018).
Deadline for application: 2021-01-16
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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