{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/regularization-path-of-cross-validation-error","title":"Regularization Path of Cross-Validation Error Lower Bounds","arxiv_id":"1502.02344","date":"2015-02-09","proceeding":"NeurIPS 2015 12","authors":["Atsushi Shibagaki","Yoshiki Suzuki","Masayuki Karasuyama","Ichiro Takeuchi"],"abstract":"Careful tuning of a regularization parameter is indispensable in many machine\nlearning tasks because it has a significant impact on generalization\nperformances. Nevertheless, current practice of regularization parameter tuning\nis more of an art than a science, e.g., it is hard to tell how many grid-points\nwould be needed in cross-validation (CV) for obtaining a solution with\nsufficiently small CV error. In this paper we propose a novel framework for\ncomputing a lower bound of the CV errors as a function of the regularization\nparameter, which we call regularization path of CV error lower bounds. The\nproposed framework can be used for providing a theoretical approximation\nguarantee on a set of solutions in the sense that how far the CV error of the\ncurrent best solution could be away from best possible CV error in the entire\nrange of the regularization parameters. We demonstrate through numerical\nexperiments that a theoretically guaranteed a choice of regularization\nparameter in the above sense is possible with reasonable computational costs.","url_abs":"http://arxiv.org/abs/1502.02344v2","url_pdf":"http://arxiv.org/pdf/1502.02344v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"regularization-path-of-cross-validation-error","repo_url":"https://github.com/takeuchi-lab/RPCVELB","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}