Name
Title | ||
|---|---|---|
Discriminatively Semi-supervised Classification with Application to Terrain Perception. |
Abstract | ||
|---|---|---|
In this paper, we focus on a new semi-supervised framework based on a new form of manifold regularization, which contains both underlying discriminative and local geometry structure of unlabeled samples, thus can mine as much underlying knowledge lurking in the unlabeled samples as possible. Meanwhile, this method introduces an equality type constraint that aims to minimize the error over the unlabeled patterns into the defining constraint structure of the proposed learning framework. The method is tested in the challenging problem of terrain perception. Results obtained demonstrate the effectiveness and feasibility of the proposed method for terrain image classification. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.3233/978-1-61499-619-4-303 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
semi-supervised learning,discriminative information,terrain image classification | Pattern recognition,Computer science,Terrain,Artificial intelligence,Perception | Conference |
Volume | ISSN | Citations |
281 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Yan | 1 | 0 | 0.68 |
| Qiaolin Ye | 2 | 397 | 27.02 |
| Cui-Ping Sun | 3 | 0 | 0.34 |
| Min-Ting Shi | 4 | 0 | 0.34 |