|
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 |
|---|
| 001 | 52402 |
|---|
| 002 | 9 |
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| 004 | 4A42FAFA-0E09-49E0-B790-92F6C555711F |
|---|
| 005 | 202409181518 |
|---|
| 008 | 081223s VN| vie |
|---|
| 009 | 1 0 |
|---|
| 039 | |y20240918152233|ztainguyendientu |
|---|
| 040 | |aACTVN |
|---|
| 041 | |avie |
|---|
| 044 | |avm |
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| 082 | |a006.3 |
|---|
| 100 | 10|aVu, Tuan Dang |
|---|
| 110 | |bVietnam Academy Of Science And Technology |
|---|
| 245 | |aGraph Based Clustering With Constraints And Active Learning / |cVu Tuan Dang, Vu Viet Vu, Do Hong Quan |
|---|
| 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 |
|---|
| 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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