A Novel Parameter Refinement Approach to One Class Support Vector Machine

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4 Downloads (Pure)

Abstract

One-Class Support Vector Machine employs a grid parameter selection process to discover the best parameters for a given data set. It is assumed that two separate trade-off parameters are assigned to normal and abnormal data samples, respectively. However, this assumption is not always true because data samples have different contributions to the construction of hypersphere or hyperplane decision boundary. In this paper, we introduce a new iterative learning process that is carried out right after the grid parameter selection process to refine the trade-off parameter value for each sample. In this learning process, a weight is assigned to each sample to represent the contribution of that sample and is iteratively refined. Experimental results performed on a number of data sets show a better performance for the proposed approach
Original languageEnglish
Title of host publicationInternational Conference on Neural Information Processing (ICONIP 2011)
Subtitle of host publicationLecture Notes in Computer Science
EditorsBao-Liang Lu, Liqing Zhang, James Kwok
Place of PublicationBerlin Heidelberg
PublisherSpringer Verlag
Pages529-536
Number of pages8
Volume7063
ISBN (Electronic)9783642249587
ISBN (Print)9783642249570
DOIs
Publication statusPublished - 2011
EventInternational Conference on Neural Information Processing ICONIP 2011 - Shanghai, Shanghai, China
Duration: 13 Nov 201117 Nov 2011

Conference

ConferenceInternational Conference on Neural Information Processing ICONIP 2011
Abbreviated titleICONIP 2011
CountryChina
CityShanghai
Period13/11/1117/11/11

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  • Cite this

    Tran, D., Ma, W., & Sharma, D. (2011). A Novel Parameter Refinement Approach to One Class Support Vector Machine. In B-L. Lu, L. Zhang, & J. Kwok (Eds.), International Conference on Neural Information Processing (ICONIP 2011): Lecture Notes in Computer Science (Vol. 7063, pp. 529-536). Springer Verlag. https://doi.org/10.1007/978-3-642-24958-7_61