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A Fast Cross-Validation Algorithm for Kernel Ridge Regression by Eigenvalue Decomposition

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/75890

Title: A Fast Cross-Validation Algorithm for Kernel Ridge Regression by Eigenvalue Decomposition
Authors: Tanaka, Akira Browse this author →KAKEN DB
Imai, Hideyuki Browse this author →KAKEN DB
Keywords: kernel ridge regression
model selection
hyperparameter
cross-validation
Issue Date: Sep-2019
Publisher: 電子情報通信学会
Journal Title: IEICE transactions on fundamentals of electronics communications and computer sciences
Issue: 9
Start Page: 1317
End Page: 1320
Publisher DOI: 10.1587/transfun.E102.A.1317
Abstract: 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.
Rights: copyright©2019 IEICE
Type: article
URI: http://hdl.handle.net/2115/75890
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 田中 章

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