Summary
|
For Full-Text PDF, please login, if you are a member of IEICE, or go to Pay Per View on menu list, if you are a nonmember of IEICE. |
|
A Fast Cross-Validation Algorithm for Kernel Ridge Regression by Eigenvalue Decomposition Akira TANAKA Hideyuki IMAI Publication IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences Vol.E102-A No.9 pp.1317-1320 Publication Date: 2019/09/01 Online ISSN: 1745-1337 DOI: 10.1587/transfun.E102.A.1317 Type of Manuscript: LETTER Category: Numerical Analysis and Optimization Keyword: kernel ridge regression, model selection, hyperparameter, cross-validation, Full Text: PDF(260.7KB)>>
Summary: 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. | ||||||||||
| ||||||||||
