| タイトル | Kernel Partial Least Squares for Nonlinear Regression and Discrimination |
| 本文（外部サイト） | http://hdl.handle.net/2060/20030014609 |
| 著者（英） | Rosipal, Roman; Clancy, Daniel |
| 著者所属（英） | NASA Ames Research Center |
| 発行日 | 2002-01-01 |
| 言語 | eng |
| 内容記述 | This paper summarizes recent results on applying the method of partial least squares (PLS) in a reproducing kernel Hilbert space (RKHS). A previously proposed kernel PLS regression model was proven to be competitive with other regularized regression methods in RKHS. The family of nonlinear kernel-based PLS models is extended by considering the kernel PLS method for discrimination. Theoretical and experimental results on a two-class discrimination problem indicate usefulness of the method. |
| NASA分類 | Numerical Analysis |
| 権利 | No Copyright |
| URI | https://repository.exst.jaxa.jp/dspace/handle/a-is/223717 |
