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A Fast Cross-Validation Algorithm for Kernel Ridge Regression by Eigenvalue Decomposition
Akira TANAKA Hideyuki IMAI
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2019/09/01
Online ISSN: 1745-1337
Type of Manuscript: LETTER
Category: Numerical Analysis and Optimization
kernel ridge regression, model selection, hyperparameter, cross-validation,
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A fast cross-validation algorithm for model selection in kernel ridge regression problems is proposed, which is aiming to further reduce the computational cost of the algorithm proposed by An et al. by eigenvalue decomposition of a Gram matrix.