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Support vector machine / Stylometry / Classifier / Binary classification / Supervised learning / Statistical hypothesis testing / Statistics / Machine learning / Statistical classification


Classify, but Verify: Breaking the Closed-World Assumption in Stylometric Authorship Attribution Ariel Stolerman, Rebekah Overdorf, Sadia Afroz and Rachel Greenstadt The Privacy, Security and Automation Lab Drexel Univer
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Document Date: 2014-02-05 17:38:49

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File Size: 2,73 MB

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Company

Spinn3r.com / Gap / /

Currency

pence / /

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Facility

Automation Lab Drexel University / Hall et al. / /

IndustryTerm

biometric authentication systems / stylometric analysis algorithm / innocent Internet user / non-handwritten communications / neural networks / internet scale authorship attribuMost previous work / cerThe web / law practitioners / online domains / Internet activists / /

Organization

Privacy / Security and Automation Lab Drexel University Philadelphia / U.S. Securities and Exchange Commission / Federal Bureau of Investigation / /

Person

Rachel Greenstadt / Lee / Brennan-Greenstadt Adversarial / Brennan Juola / Cho / Rebekah Overdorf / /

Position

author / forensic analyst / chosen author / security guard / Per-Author / chosen author / namely P1 / using author / test author and the predicted author / /

ProvinceOrState

South Dakota / /

PublishedMedium

Machine Learning / /

Technology

artificial intelligence / 2009 Technology / API / Writeprints algorithm / Machine Learning / unmasking algorithm / stylometric analysis algorithm / /

URL

http /

SocialTag