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Evaluating classification accuracy for modern learning approaches
dc.contributor.authorLi, Jialiang
dc.contributor.authorGao, Ming
dc.contributor.authorD’Agostino, Ralph
dc.date.accessioned2019-05-31T18:27:48Z
dc.date.availableWITHHELD_14_MONTHS
dc.date.available2019-05-31T18:27:48Z
dc.date.issued2019-06-15
dc.identifier.citationLi, Jialiang; Gao, Ming; D’Agostino, Ralph (2019). "Evaluating classification accuracy for modern learning approaches." Statistics in Medicine 38(13): 2477-2503.
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttps://hdl.handle.net/2027.42/149333
dc.publisherJohn Wiley & Sons
dc.subject.otherconvolutional neural net
dc.subject.otherdeep learning
dc.subject.otherR package
dc.subject.otherneural network
dc.subject.othermxnet
dc.subject.othermultilayer perceptron
dc.titleEvaluating classification accuracy for modern learning approaches
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbsecondlevelPublic Health
dc.subject.hlbtoplevelScience
dc.subject.hlbtoplevelHealth Sciences
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/149333/1/sim8103_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/149333/2/sim8103.pdf
dc.identifier.doi10.1002/sim.8103
dc.identifier.sourceStatistics in Medicine
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dc.owningcollnameInterdisciplinary and Peer-Reviewed


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