Name
Title | ||
|---|---|---|
Double regularization methods for robust feature selection and SVM classification via DC programming. |
Abstract | ||
|---|---|---|
•Novel embedded feature selection approaches for SVM.•Robust SVM formulations based on second-order cone programming.•The l1 and the l0 penalties are used in combination with the l2 regularization.•DC programming is used for solving a nonconvex SOCP model.•Superior performance is achieved in experiments on high-dimensional datasets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ins.2017.11.035 | Information Sciences |
Keywords | Field | DocType |
Zero norm,Support vector machines,Second-order cone programming,Dc algorithm | Tikhonov regularization,Second-order cone programming,Pattern recognition,Feature selection,Word error rate,Support vector machine,Regular polygon,Regularization (mathematics),Artificial intelligence,Machine learning,Mathematics,Regularization perspectives on support vector machines | Journal |
Volume | Issue | ISSN |
429 | C | 0020-0255 |
Citations | PageRank | References |
2 | 0.37 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julio López | 1 | 124 | 13.49 |
| Sebastián Maldonado | 2 | 508 | 32.45 |
| Miguel Carrasco | 3 | 21 | 4.35 |