Name
Abstract | ||
|---|---|---|
Semisupervised learning methods are often adopted to handle datasets with very small number of labeled samples. However, conventional semisupervised ensemble learning approaches have two limitations: 1) most of them cannot obtain satisfactory results on high dimensional datasets with limited labels and 2) they usually do not consider how to use an optimization process to enlarge the training set. ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCYB.2017.2651114 | IEEE Transactions on Cybernetics |
Keywords | Field | DocType |
Semisupervised learning,Training,Algorithm design and analysis,Robustness,Data mining,Boosting | Semi-supervised learning,Stability (learning theory),Pattern recognition,Subspace topology,Active learning (machine learning),Computer science,Robustness (computer science),Nonparametric statistics,Boosting (machine learning),Artificial intelligence,Ensemble learning,Machine learning | Journal |
Volume | Issue | ISSN |
48 | 2 | 2168-2267 |
Citations | PageRank | References |
3 | 0.37 | 76 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiwen Yu | 1 | 231 | 18.51 |
| Ye Lu | 2 | 9 | 3.54 |
| Jun Zhang | 3 | 2491 | 127.27 |
| Jane You | 4 | 1885 | 136.93 |
| Hau-San Wong | 5 | 1008 | 86.89 |
| Yide Wang | 6 | 334 | 47.29 |
| Guoqiang Han | 7 | 439 | 43.27 |