Name
Abstract | ||
|---|---|---|
This paper presents a data domain description formed by the minimum volume covering ellipsoid around a dataset, called “ellipsoidal support vector data description (eSVDD).” The method is analogous to support vector data description (SVDD), but instead, with an ellipsoidal domain description allowing tighter space around the data. In eSVDD, a hyperellipsoid extends its ability to describe more complex data patterns by kernel methods. This is explicitly achieved by defining an “empirical feature map” to project the images of given samples to a higher-dimensional space. We compare the performance of the kernelized ellipsoid in one-class classification with SVDD using standard datasets. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s00521-016-2343-3 | Neural Computing and Applications |
Keywords | Field | DocType |
Kernel minimum volume covering ellipsoid, Ellipsoidal support vector data description, Domain data description, Empirical feature space | Mathematical optimization,Ellipsoid,Data domain,Pattern recognition,Support vector machine,Complex data type,Artificial intelligence,Kernel method,Mathematics,Data description | Journal |
Volume | Issue | ISSN |
28 | S-1 | 1433-3058 |
Citations | PageRank | References |
3 | 0.41 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kasemsit Teeyapan | 1 | 3 | 0.41 |
| Nipon Theera-umpon | 2 | 184 | 30.59 |
| S. Auephanwiriyakul | 3 | 246 | 39.45 |