Name
Title | ||
|---|---|---|
Simultaneous Safe Feature and Sample Elimination for Sparse Support Vector Regression. |
Abstract | ||
|---|---|---|
Sparse support vector regression (SSVR) is an effective regression technique. It has been successfully applied to many practical problems. However, it remains challenging to handle the large-scale problems. A nice property of SSVR is double sparsity in the sense that most irrelevant features and samples have no effect on the regressor. Inspired by it, we propose a simultaneous safe feature and sam... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TSP.2019.2924580 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Support vector machines,Acceleration,Training,Computational modeling,Signal processing algorithms,Safety,Linear programming | Rule-based system,Duality gap,Convexity,Mathematical optimization,Regression,Support vector machine,Algorithm,Linear programming,Solver,Mathematics,Speedup | Journal |
Volume | Issue | ISSN |
67 | 15 | 1053-587X |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongmei Wang | 1 | 31 | 13.44 |
| Xianli Pan | 2 | 112 | 7.39 |
| Yitian Xu | 3 | 489 | 35.06 |