Name
Abstract | ||
|---|---|---|
•Propose a kernel-free double-well potential SVM for nonlinear binary classification.•Analyze the theoretical properties of the proposed model.•The sequential minimal optimization algorithm is adopted to solve the proposed model.•Conduct computational experiments on artificial, benchmark and real-life data.•The new model outperforms well-known SVM models for accurate classification. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.ejor.2020.10.040 | European Journal of Operational Research |
Keywords | DocType | Volume |
Data science,Support vector machine,Double well potential function,Kernel-free SVM,Binary classification | Journal | 290 |
Issue | ISSN | Citations |
1 | 0377-2217 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheming Gao | 1 | 0 | 0.34 |
| Shu-Cherng Fang | 2 | 1153 | 95.41 |
| Jian Luo | 3 | 14 | 3.97 |
| Negash Medhin | 4 | 0 | 0.68 |