| 作成者 |
|
|
|
|
|
| 本文言語 |
|
| 出版者 |
|
| 発行日 |
|
| 収録物名 |
|
| 巻 |
|
| 出版タイプ |
|
| アクセス権 |
|
| 関連DOI |
|
| 関連DOI |
|
|
|
| 関連URI |
|
|
|
| 関連URI |
|
| 関連HDL |
|
| 関連情報 |
|
|
|
| 概要 |
We consider the problem of constructing nonlinear regression models with Gaussian basis functions, using lasso regularization. Regularization with a lasso penalty is an advantageous in that it reduces... some unknown parameters in linear regression models toward exactly zero. We propose imposing a weighted lasso penalty on a nonlinear regression model and thereby selecting the number of basis functions effectively. In order to select tuning parameters in the regularization method, we use model selection criteria derived from information-theoretic and Bayesian viewpoints. Simulation results demonstrate that our methodology performs well in various situations.続きを見る
|
Mendeley出力
