Kernel Methods in Anomaly Detection

The objectives of this research is about evaluation of accuracy and performance of different kernel methods. The results of these methods are demonstrated on credit card fraud dataset to show superiority of one-class SVM (Super Vextor Machine) for anomaly detection problem.

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Main Author: Hejazi, Maryamsadat
Format: Thesis
Published: 2012
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id my-mmu-ep.3646
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spelling my-mmu-ep.36462012-11-27T01:34:49Z Kernel Methods in Anomaly Detection 2012-01 Hejazi, Maryamsadat QA76.75-76.765 Computer software The objectives of this research is about evaluation of accuracy and performance of different kernel methods. The results of these methods are demonstrated on credit card fraud dataset to show superiority of one-class SVM (Super Vextor Machine) for anomaly detection problem. 2012-01 Thesis http://shdl.mmu.edu.my/3646/ http://vlib.mmu.edu.my/diglib/login/dlusr/login.php masters Multimedia University Faculty of Computing and Informatics
institution Multimedia University
collection MMU Institutional Repository
topic QA76.75-76.765 Computer software
spellingShingle QA76.75-76.765 Computer software
Hejazi, Maryamsadat
Kernel Methods in Anomaly Detection
description The objectives of this research is about evaluation of accuracy and performance of different kernel methods. The results of these methods are demonstrated on credit card fraud dataset to show superiority of one-class SVM (Super Vextor Machine) for anomaly detection problem.
format Thesis
qualification_level Master's degree
author Hejazi, Maryamsadat
author_facet Hejazi, Maryamsadat
author_sort Hejazi, Maryamsadat
title Kernel Methods in Anomaly Detection
title_short Kernel Methods in Anomaly Detection
title_full Kernel Methods in Anomaly Detection
title_fullStr Kernel Methods in Anomaly Detection
title_full_unstemmed Kernel Methods in Anomaly Detection
title_sort kernel methods in anomaly detection
granting_institution Multimedia University
granting_department Faculty of Computing and Informatics
publishDate 2012
_version_ 1747829534244208640