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On Quantile Regression in Reproducing Kernel Hilbert Spaces with Data Sparsity Constraint
For spline regressions, it is well known that the choice of knots is crucial for the performance of the estimator. As a general learning framework covering the smoothing splines, learning in a Reproducing Kernel Hilbert Space (RKHS) has a similar issue. However, the selection of training data points...
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| Publicat a: | J Mach Learn Res |
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| Autors principals: | , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
2016
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4850041/ https://ncbi.nlm.nih.gov/pubmed/27134575 |
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