Name
Abstract | ||
|---|---|---|
We propose an adaptive scheme for distributed learning of nonlinear functions by a network of nodes. The proposed algorithm consists of a local adaptation stage utilizing multiple kernels with projections onto hyperslabs and a diffusion stage to achieve consensus on the estimates over the whole network. Multiple kernels are incorporated to enhance the approximation of functions with several high- ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TSP.2018.2868040 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Kernel,Measurement,Signal processing algorithms,Convergence,Dictionaries,Adaptive learning,Indexes | Convergence (routing),Kernel (linear algebra),Hilbert space,Mathematical optimization,Matrix (mathematics),Cartesian product,Equivalence (measure theory),Kernel adaptive filter,Adaptive learning,Mathematics | Journal |
Volume | Issue | ISSN |
66 | 21 | 1053-587X |
Citations | PageRank | References |
1 | 0.38 | 26 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ban-Sok Shin | 1 | 8 | 2.50 |
| Masahiro Yukawa | 2 | 272 | 30.44 |
| Renato L. G. Cavalcante | 3 | 180 | 24.21 |
| Armin Dekorsy | 4 | 513 | 57.91 |