Name
Abstract | ||
|---|---|---|
Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments however there is no theoretical work on comparison between these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points to generate a general decision boundary which can be transformed to hyperplane for SVM or hypersphere for SVDD. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IJCNN.2012.6252642 | IJCNN |
Keywords | Field | DocType |
hyperplane,one-class classification,data description,pattern classification,svm,spherically shaped boundary,support vector machine,kernel-based methods,svdd,optimal hypersphere,novelty detection,support vector data description,general decision boundary,optimal points,support vector machines,optimization,vectors,one class classification,mathematical model,trajectory | Kernel (linear algebra),Structured support vector machine,One-class classification,Least squares support vector machine,Pattern recognition,Computer science,Support vector machine,Hypersphere,Artificial intelligence,Relevance vector machine,Decision boundary,Machine learning | Conference |
ISSN | ISBN | Citations |
2161-4393 E-ISBN : 978-1-4673-1489-3 | 978-1-4673-1489-3 | 1 |
PageRank | References | Authors |
0.36 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Trung Le | 1 | 92 | 17.72 |
| Dat Tran | 2 | 454 | 78.64 |
| Wanli Ma | 3 | 270 | 32.72 |
| Dharmendra Sharma | 4 | 240 | 58.91 |