Two Methodologies for Predicting Patent Litigation Outcomes: Logistic Regression Versus Classification Trees
2016; Wiley; Volume: 53; Issue: 1 Linguagem: Inglês
10.1111/ablj.12075
ISSN1744-1714
Tópico(s)Law, AI, and Intellectual Property
ResumoAmerican Business Law JournalVolume 53, Issue 1 p. 193-193 ErratumFree Access Two Methodologies for Predicting Patent Litigation Outcomes: Logistic Regression Versus Classification Trees This article corrects the following: Two Methodologies for Predicting Patent Litigation Outcomes: Logistic Regression Versus Classification Trees Tammy W. Cowart, Roger Lirely, Sherry Avery, Volume 51Issue 4American Business Law Journal pages: 843-877 First Published online: November 18, 2014 First published: 21 January 2016 https://doi.org/10.1111/ablj.12075AboutSectionsPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat In Tammy W. Cowart et al.,1 an error was published in section VI, “APPLICATION”. The editors of the American Business Law Journal regret that an error appeared on page 873 in issue 4 of volume 51. Although Lexis identifies Mr. Elsberg as Samsung's lead attorney pro hac vice in its publications of the January 9, 2014 and November 25, 2014 district court decisions in this case, Mr. Elsberg has informed the authors he was not, in fact, lead trial attorney. We apologize for any misunderstanding. Footnotes 1Tammy W. Cowart et al., Two Methodologies for Predicting Patent Litigation Outcomes: Logistic Regression Versus Classification Trees, 51 Am. Bus. L. J. 843, 873 & n. 134 (2014), DOI: 10.1111/ablj.12036. Volume53, Issue1Spring 2016Pages 193-193 ReferencesRelatedInformation
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