Open AccessR1SVM: A Randomised Nonlinear Approach to Large-Scale Anomaly Detection
Author(s) -
Sarah M. Erfani,
Mahsa Baktashmotlagh,
Sutharshan Rajasegarar,
Shanika Karunasekera,
Christopher Leckie
Publication year - 2015
Publication title -
proceedings of the aaai conference on artificial intelligence
Language(s) - Uncategorized
Resource type - Journals
eISSN - 2374-3468
pISSN - 2159-5399
DOI - 10.1609/aaai.v29i1.9208
Subject(s) - anomaly detection , support vector machine , computer science , leverage (statistics) , artificial intelligence , kernel (algebra) , scale (ratio) , machine learning , nonlinear system , class (philosophy) , anomaly (physics) , pattern recognition (psychology) , data mining , mathematics , physics , quantum mechanics , combinatorics , condensed matter physics
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