Name
Abstract | ||
|---|---|---|
We present a semi-supervised learning algorithm to recognize feature vector noises in the training data. Our proposal employs an active contour model technology (ACM) which is used for objects extraction in the field of computer vision. We extend the ACM technology to the similarity formula of our proposal for identifying feature vector noises in the training set and improve the performance of the training data. The proposal is applied to the synthetic data and real data. The experiments prove that the proposal has a high performance on the feature vector noises in the unlabeled data of the training set. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 11TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC) | semi-supervised learning, active contours model, feature vector noises |
Field | DocType | Citations |
Training set,Active contour model,Feature vector,Semi-supervised learning,Embedding,Pattern recognition,Computer science,Supervised learning,Synthetic data,Artificial intelligence,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 12 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiwei Du | 1 | 3 | 4.10 |
| Yi-Peng Liu | 2 | 0 | 0.68 |