Name
Abstract | ||
|---|---|---|
This work tackles the problem of learning a robust classification function from a very small sample set when a related but unlabeled data set is provided. To this end we define a new semi-supervised method that is based on a stability criterion. We successfully apply our proposal in the specific case of automatic diagnosis of intestinal motility disease using video capsule endoscopy. An experimental evaluation shows the viability to apply the proposed method in motility disfunction diagnosis. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-76725-1_80 | CIARP |
Keywords | Field | DocType |
automatic diagnosis,intestinal motility disease,experimental evaluation,small sample,unlabeled data set,semi-supervised learning method,new semi-supervised method,robust classification function,motility disfunction diagnosis,specific case,semi supervised learning,feature extraction | Computer vision,Stability criterion,Semi-supervised learning,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Capsule endoscopy,Motility | Conference |
Volume | ISSN | ISBN |
4756 | 0302-9743 | 3-540-76724-X |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Santi Seguí | 1 | 85 | 9.11 |
| Laura Igual | 2 | 266 | 18.41 |
| Petia Radeva | 3 | 1684 | 153.53 |
| Carolina Malagelada | 4 | 44 | 5.77 |
| Fernando Azpiroz | 5 | 74 | 8.02 |
| Jordi Vitrià | 6 | 737 | 98.14 |