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Kernel partial least squares for nonlinear regression and discrimination
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Rosipal, Roman |
| Copyright Year | 2002 |
| Description | 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. |
| File Size | 537797 |
| Page Count | 12 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_20030014609 |
| Archival Resource Key | ark:/13960/t52g2pc7z |
| Language | English |
| Publisher Date | 2002-01-01 |
| Access Restriction | Open |
| Subject Keyword | Numerical Analysis Kernel Functions Least Squares Method Discriminant Analysis Statistics Vectors Mathematics Nonlinearity Hilbert Space Mathematical Models Regression Analysis Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
