Name
Abstract | ||
|---|---|---|
We propose a robust recurrent kernel online learning (RRKOL) algorithm based on the celebrated real-time recurrent learning approach that exploits the kernel trick in a recurrent online training manner. The novel RRKOL algorithm guarantees weight convergence with regularized risk management through the use of adaptive recurrent hyperparameters for superior generalization performance. Based on a ne... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TNNLS.2016.2518223 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Kernel,Convergence,Algorithm design and analysis,Robust stability,Prediction algorithms,Signal processing algorithms,Training | Convergence (routing),Kernel (linear algebra),Online learning,Algorithm design,Pattern recognition,Hyperparameter,Radial basis function kernel,Computer science,Stability proof,Artificial intelligence,Kernel method,Machine learning | Journal |
Volume | Issue | ISSN |
28 | 5 | 2162-237X |
Citations | PageRank | References |
3 | 0.38 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Q. Song | 1 | 65 | 6.02 |
| Zhao Xu | 2 | 235 | 32.01 |
| Haijin Fan | 3 | 61 | 4.77 |
| Danwei Wang | 4 | 1529 | 175.13 |