DDC
| 006.3 |
Tác giả CN
| Vu, Tuan Dang |
Tác giả TT
| |
Nhan đề
| Graph Based Clustering With Constraints And Active Learning / Vu Tuan Dang, Vu Viet Vu, Do Hong Quan |
Tóm tắt
| During the past few years, semi-supervised clustering has emerged as adirection in machine learning research. In a semi-supervised clustering algorithm. tie frying results can be significantly improved by using side information, which is available or Grille; e-d from users. There are two main kinds of side information that can be learned in semi-superfse- lose-ring algorithms including class labels(seeds) or pairwise constraints. In this paper. we trig supervised graph based clustering algorithm that tries to use seeds and constraints 1 .process, called MCSSGC. Moreover, we also introduce a simple but efficient active {-eamethod to collect the constraints that can boost the performance of MCSSGC, named. Three obtained results Show that the proposed algorithm can significantly improve the compared to some recent algorithms. |
Từ khóa tự do
| Active learning |
Từ khóa tự do
| Constraints |
Từ khóa tự do
| Semi-supervised clustering |
Tác giả(bs) CN
| Do, Hong Quan |
Tác giả(bs) CN
| Vu, Viet Vu |
Nguồn trích
| Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics 2021tr. 73-91
Số: 01 |
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000
| 00000nab#a2200000ui#4500 |
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001 | 52402 |
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002 | 9 |
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004 | 4A42FAFA-0E09-49E0-B790-92F6C555711F |
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005 | 202409181518 |
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008 | 081223s VN| vie |
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009 | 1 0 |
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039 | |y20240918152233|ztainguyendientu |
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040 | |aACTVN |
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041 | |avie |
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044 | |avm |
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082 | |a006.3 |
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100 | 10|aVu, Tuan Dang |
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110 | |bVietnam Academy Of Science And Technology |
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245 | |aGraph Based Clustering With Constraints And Active Learning / |cVu Tuan Dang, Vu Viet Vu, Do Hong Quan |
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520 | |aDuring the past few years, semi-supervised clustering has emerged as adirection in machine learning research. In a semi-supervised clustering algorithm. tie frying results can be significantly improved by using side information, which is available or Grille; e-d from users. There are two main kinds of side information that can be learned in semi-superfse- lose-ring algorithms including class labels(seeds) or pairwise constraints. In this paper. we trig supervised graph based clustering algorithm that tries to use seeds and constraints 1 .process, called MCSSGC. Moreover, we also introduce a simple but efficient active {-eamethod to collect the constraints that can boost the performance of MCSSGC, named. Three obtained results Show that the proposed algorithm can significantly improve the compared to some recent algorithms. |
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653 | |aActive learning |
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653 | |aConstraints |
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653 | |aSemi-supervised clustering |
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700 | |aDo, Hong Quan |
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700 | |aVu, Viet Vu |
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773 | 0 |tTạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics |d2021|gtr. 73-91|x1813-9663|i01 |
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890 | |a0|b0|c1|d0 |
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