Name
Abstract | ||
|---|---|---|
The least-square regression problem is considered by coefficient-based regularization schemes with ℓ1驴驴penalty. The learning algorithm is analyzed with samples drawn from unbounded sampling processes. The purpose of this paper is to present an elaborate concentration estimate for the algorithms by means of a novel stepping stone technique. The learning rates derived from our analysis can be achieved in a more general setting. Our refined analysis will lead to satisfactory learning rates even for non-smooth kernels. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s10444-012-9288-6 | Adv. Comput. Math. |
Keywords | DocType | Volume |
Learning theory,Coefficient-based regularization and ℓ,1,-penalty,Unbounded sampling processes,Concentration estimate for error analysis,68T05,62J02 | Journal | 39 |
Issue | ISSN | Citations |
3-4 | 1019-7168 | 2 |
PageRank | References | Authors |
0.39 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng-Chu Guo | 1 | 26 | 2.66 |
| Lei Shi | 2 | 104 | 8.13 |