Name
Abstract | ||
|---|---|---|
The goal of this paper is the development of a novel efficient online kernel-based algorithm for classification. The spirit of the algorithm stems from the recently introduced adaptive projected subgradient method. This is a general convex analytic tool that employs projections onto a sequence of convex sets and it can be considered as a generalization of the celebrated APA algorithm, widely used in classical adaptive filtering. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICASSP.2007.366263 | Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference |
Keywords | Field | DocType |
adaptive filters,pattern classification,set theory,APA algorithm,adaptive filtering,adaptive projected subgradient method,convex set sequence,general convex analytic tool,online kernel-based classification,Adaptive systems,Pattern classification | Kernel (linear algebra),Hilbert space,Set theory,Mathematical optimization,Algorithm design,Pattern recognition,Computer science,Adaptive system,Regular polygon,Adaptive filter,Artificial intelligence,Statistical classification | Conference |
Volume | ISSN | ISBN |
2 | 1520-6149 | 1-4244-0727-3 |
Citations | PageRank | References |
2 | 0.47 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konstantinos Slavakis | 1 | 2 | 0.47 |
| Sergios Theodoridis | 2 | 59 | 3.13 |
| Isao Yamada | 3 | 58 | 3.74 |