Name
Abstract | ||
|---|---|---|
We propose a new cross-training based learning algorithm in this paper. This algorithm generates three classifiers based on the three subsets of original labeled and unlabeled training set. The proposed algorithm is evaluated using data from the UCI repository by the experiment. Experimental results show that our algorithm can improve classification accuracy compared to those of other algorithms. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1529282.1529487 | SAC |
Keywords | Field | DocType |
new cross-training approach,unlabeled training set,new cross-training,classification accuracy,uci repository,proposed algorithm,semi supervised learning | Training set,Semi-supervised learning,Stability (learning theory),Pattern recognition,Computer science,Wake-sleep algorithm,Co-training,Supervised learning,Artificial intelligence,Labeled data,Machine learning,Cross-training | Conference |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongshan Huang | 1 | 18 | 1.49 |
| Enmin Song | 2 | 176 | 24.53 |
| Guangzhi Ma | 3 | 24 | 5.32 |
| Huirong Zhan | 4 | 0 | 0.34 |
| Chih-Cheng Hung | 5 | 0 | 0.34 |