Name
Abstract | ||
|---|---|---|
Top-k performance has recently received increasing attention in large data categories. Advances, like a top-k multiclass support vector machine (SVM), have consistently improved the top-k accuracy. However, the key ingredient in the state-of-the-art optimization scheme based upon stochastic dual coordinate ascent relies on the sorting method, which yields O(d log d) complexity. In this paper, we l... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2018.2826039 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Support vector machines,Optimization,Training,Sorting,Newton method,Fasteners,Learning systems | Superlinear convergence,Data set,Computer science,Support vector machine,Algorithm,Sorting,Newton's method | Journal |
Volume | Issue | ISSN |
29 | 12 | 2162-237X |
Citations | PageRank | References |
0 | 0.34 | 26 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dejun Chu | 1 | 0 | 0.34 |
| Rui Lu | 2 | 3 | 2.73 |
| Jin Li | 3 | 61 | 25.54 |
| Xintong Yu | 4 | 0 | 2.03 |
| Changshui Zhang | 5 | 5506 | 323.40 |
| Qing Tao | 6 | 82 | 6.26 |