Enhancing one - class support vector machines for unsupervised anomaly detection / Mennatallah Amer ; Supervised Markus Goldstein , Andreas Dengel , Slim Abdennadher
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TextLanguage: English Publication details: Cairo : Mennat Allah Amer , 2013Description: 77 Leaves ; 30cmDissertation note: Thesis (M.Sc.) - German University - Faculty of Postgraduate Studies and Scientific Research - Department of Computer Science and Engineering Summary: Support vector machines (SVMs) have been one of the most prominent machine learning techniques for the past decade. In this thesis, the effectiveness of applying SVMs for derecting outliers in an unsupervised setting is investigated. Unsupervised anomaly detection techniques operate directly on an unseen dataset, under the assumption that outhers are sparsely present in it
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Thesis
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قاعة الثقاقات الاجنبية - الدور الثالث | المكتبة المركزبة الجديدة - جامعة القاهرة | Cai01.34.M.Sc.2013.Me.E (Browse shelf(Opens below)) | Not for loan | 01010110063584000 |
Thesis (M.Sc.) - German University - Faculty of Postgraduate Studies and Scientific Research - Department of Computer Science and Engineering
Support vector machines (SVMs) have been one of the most prominent machine learning techniques for the past decade. In this thesis, the effectiveness of applying SVMs for derecting outliers in an unsupervised setting is investigated. Unsupervised anomaly detection techniques operate directly on an unseen dataset, under the assumption that outhers are sparsely present in it
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