Randomized nonlinear one-class support vector machines with bounded loss function to detect of outliers for large scale IoT data Razzak, Muhammad Imran, Zafar, Khurram, Imran, Muhammad and Xu, Guandong 2020, Randomized nonlinear one-class support vector machines with bounded loss function to detect of outliers for large scale IoT data, Future generation computer systems, vol. 112, pp. 715-723, doi: 10.1016/j.future.2020.05.045. Attached Files Name Description MIMEType Size Downloads Title Randomized nonlinear one-class support vector machines with bounded loss function to detect of outliers for large scale IoT data Author(s) Razzak, Muhammad Imran orcid.org/0000-0002-3930-6600 Zafar, Khurram Imran, Muhammad Xu, Guandong Journal name Future generation computer systems Volume number 112 Start page 715 End page 723 Total pages 9 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2020 ISSN 0167-739X Keyword(s) Anomaly detection Hinge loss Support Vector Machines Randomized SVM One-class classification Language eng DOI 10.1016/j.future.2020.05.045 Indigenous content off Field of Research 0805 Distributed Computing 0806 Information Systems HERDC Research category C1 Refereed article in a scholarly journal Persistent URL http://hdl.handle.net/10536/DRO/DU:30139670 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 06 Jul 2020, 10:12:46 EST Tue, 07 Jul 2020, 05:00:26 EST Tue, 07 Jul 2020, 06:03:26 EST Fri, 10 Jul 2020, 09:11:26 EST Fri, 10 Jul 2020, 09:13:39 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 22 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Mon, 06 Jul 2020, 10:12:46 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.