Name
Abstract | ||
|---|---|---|
In this paper, the problem of implicit online learning is considered. A tighter convergence bound is derived, which demonstrates theoretically the feasibility of implicit update for online learning. Then we combine SMD with implicit update technique and the resulting algorithm possesses the inherent stability. Theoretical result is well corroborated by the experiments we performed which also indicate that combining SMD with implicit update technique is another promising way for online learning. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IJCNN.2008.4634288 | 2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8 |
Keywords | Field | DocType |
smd,stochastic processes,helium,hilbert space,kernel,convergence,classification algorithms,stability | Online learning,Convergence (routing),Kernel (linear algebra),Computer aided instruction,Computer science,Stochastic process,Theoretical computer science,Artificial intelligence,Statistical classification,Metacomputing,Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
17 | 2 | |