Name
Abstract | ||
|---|---|---|
The solution of the ordinary kernel ridge regression, based on the squared loss function and the squared norm-based regularizer, can be easily interpreted as a stochastic linear estimator by considering the autocorrelation prior for an unknown true function. As is well known, a stochastic affine estimator is one of the simplest extensions of the stochastic linear estimator. However, its corresponding kernel regression problem is not revealed so far. In this paper, we give a formulation of the kernel regression problem, whose solution is reduced to a stochastic affine estimator, and also give interpretations of the formulation. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1587/transinf.2021EDP7156 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | DocType | Volume |
kernel regression, autocorrelation prior, linear estimators, affine estimators, optimization criterion | Journal | E105D |
Issue | ISSN | Citations |
1 | 1745-1361 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akira Tanaka | 1 | 0 | 0.34 |
| Masanari Nakamura | 2 | 0 | 2.03 |
| Hideyuki Imai | 3 | 0 | 0.34 |