Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression

Kernel methods provide a principled approach to nonparametric learning. While their basic implementations scale poorly to large problems, recent advances showed that approximate solvers can efficiently handle massive datasets. A shortcoming of these solutions is that hyperparameter tuning is not tak...

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Veröffentlicht in:	arXiv.org 2022-01
Hauptverfasser:	Meanti, Giacomo, Carratino, Luigi, De Vito, Ernesto, Rosasco, Lorenzo
Format:	Artikel
Sprache:	eng
Schlagworte:	Empirical analysis Kernels Massive data points Optimization Regularization Tuning
Online-Zugang:	Volltext
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