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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Date: 2014-02-05 17:38:49 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 Add to Reading List Source URL: www.stolerman.net Download Document from Source Website File Size: 2,73 MB Share Document on Facebook

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