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Low-Precision Random Fourier Features for Memory-Constrained Kernel Approximation
We investigate how to train kernel approximation methods that generalize well under a memory budget. Building on recent theoretical work, we define a measure of kernel approximation error which we find to be more predictive of the empirical generalization performance of kernel approximation methods...
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| Publicado no: | Proc Mach Learn Res |
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| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6879383/ https://ncbi.nlm.nih.gov/pubmed/31777846 |
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