Abstract
This paper presents a novel graph-based algorithm for solv- ing the semi-supervised learning problem. The graph-based algorithm makes use of the recent advances in stochastic graph sampling technqiue and a modeling of the labeling consistency in semi-supervised learning. The quality of the algorithm is empirically evaluated on a synthetic clus- tering problem. The semi-supervised clustering is also applied to the problem of symptoms classification in medical image database and shows promising results.
Original language | English |
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Title of host publication | Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings |
Editors | David Cheung, Graham J. Williams, Qing Li |
Publisher | Springer Verlag |
Pages | 154-160 |
Number of pages | 7 |
ISBN (Print) | 3540419101, 9783540419105 |
DOIs | |
Publication status | Published - 2001 |
Event | 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001 - Hong Kong, Hong Kong Duration: 16 Apr 2001 → 18 Apr 2001 https://link.springer.com/book/10.1007/3-540-45357-1 (Conference Proceedings) |
Publication series
Name | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) |
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Volume | 2035 |
ISSN (Print) | 0302-9743 |
Conference
Conference | 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001 |
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Country/Territory | Hong Kong |
City | Hong Kong |
Period | 16/04/01 → 18/04/01 |
Internet address |
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Scopus Subject Areas
- Theoretical Computer Science
- General Computer Science