The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization

Kobak, Dmitry; Lomond, Jonathan; Sanchez, Benoit

dc.contributor.author	Kobak, Dmitry
dc.date.accessioned	2021-06-16T13:35:51Z
dc.date.available	2021-06-16T13:35:51Z
dc.date.issued	2020
dc.identifier.issn	1532-4435
dc.identifier.uri	http://hdl.handle.net/10900/116322
dc.language.iso	en	de_DE
dc.publisher	Microtome Publ	de_DE
dc.subject.ddc	004	de_DE
dc.subject.ddc	600	de_DE
dc.title	The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization	de_DE
dc.type	Article	de_DE
utue.quellen.id	20210304112512_01875
utue.personen.roh	Kobak, Dmitry
utue.personen.roh	Lomond, Jonathan
utue.personen.roh	Sanchez, Benoit
dcterms.isPartOf.ZSTitelID	Journal of Machine Learning Research	de_DE
dcterms.isPartOf.ZS-Issue	Article 169	de_DE
dcterms.isPartOf.ZS-Volume	21	de_DE
utue.fakultaet	04 Medizinische Fakultät	de_DE