Machine learning in forensic applications

dc.contributor.author Carriquiry, Alicia
dc.contributor.author Hofmann, Heike
dc.contributor.author Tai, Xiao Hui
dc.contributor.author VanderPlas, Susan
dc.contributor.department Center for Statistics and Applications in Forensic Evidence
dc.contributor.department Statistics
dc.date 2020-09-03T16:16:14.000
dc.date.accessioned 2021-02-25T00:41:52Z
dc.date.available 2021-02-25T00:41:52Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2019
dc.date.issued 2019-04-01
dc.description.abstract <p>The 2009 National Academy of Sciences report found pattern‐evidence disciplines to be rife with subjectivity. In the decade since, machine learning methods have been developed to try to address that issue. By Alicia Carriquiry, Heike Hofmann, Xiao Hui Tai and Susan VanderPlas.</p>
dc.description.comments <p>This following article is published as Carriquiry, Alicia, Heike Hofmann, Xiao Hui Tai, and Susan VanderPlas. "Machine learning in forensic applications." <em>Significance</em> 16, no. 2 (2019): 29-35. Posted with permission of CSAFE.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/csafe_pubs/68/
dc.identifier.articleid 1067
dc.identifier.contextkey 19233099
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath csafe_pubs/68
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/93877
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/csafe_pubs/68/0-2019_Carriquiry_MachineLearning_Permission.pdf|||Sat Jan 15 01:29:02 UTC 2022
dc.source.bitstream archive/lib.dr.iastate.edu/csafe_pubs/68/j.1740_9713.2019.01252.x.pdf|||Sat Jan 15 01:29:04 UTC 2022
dc.source.uri 10.1111/j.1740-9713.2019.01252.x
dc.subject.disciplines Legal Studies
dc.supplemental.bitstream 2019_Carriquiry_MachineLearning_Permission.pdf
dc.title Machine learning in forensic applications
dc.type article
dc.type.genre article
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