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 (LAS) | |
| dc.contributor.department | Center for Statistics and Applications in Forensic Evidence (CSAFE)" | |
| 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 | |
| dspace.entity.type | Publication | |
| relation.isOrgUnitOfPublication | d8a3c72b-850f-40f6-87c4-8812547080c7 | |
| relation.isOrgUnitOfPublication | 264904d9-9e66-4169-8e11-034e537ddbca |