Name
Title | ||
|---|---|---|
Infinite Bayesian one-class support vector machine based on Dirichlet process mixture clustering. |
Abstract | ||
|---|---|---|
•We develop a novel OCC method based on the DPM clustering and the modified OCSVMs.•Our method combines the reconstruction-based method and the boundary-based method.•The reconstruction and bounding construction are jointly optimized in a Bayesian frame.•Our method can improve the robustnessas well as the accuracy of classification performance.•Experimental results show our method performs better than other related methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.patcog.2018.01.006 | Pattern Recognition |
Keywords | Field | DocType |
Dirichlet process mixture,One-class classifiers,One-class support vector machine,Gibbs sampling | Kernel (linear algebra),Linear separability,Feature vector,Pattern recognition,Support vector machine,Artificial intelligence,Classifier (linguistics),Cluster analysis,Mathematics,Gibbs sampling,Bayesian probability | Journal |
Volume | Issue | ISSN |
78 | 1 | 0031-3203 |
Citations | PageRank | References |
2 | 0.50 | 19 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Zhang | 1 | 2 | 0.50 |
| Lan Du | 2 | 272 | 34.83 |
| Liling Li | 3 | 2 | 0.50 |
| Xuefeng Zhang | 4 | 17 | 4.83 |
| Hongwei Liu | 5 | 2 | 0.50 |