A novel parameter refinement approach to one class support vector machine

Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)


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 publicationNeural Information Processing
Subtitle of host publication18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part II
EditorsBao-Liang Lu, Liqing Zhang, James Kwok
Place of PublicationBerlin Germany
Number of pages8
ISBN (Print)9783642249570
Publication statusPublished - 2011
Externally publishedYes
EventInternational Conference on Neural Information Processing 2011 - Shanghai, China
Duration: 13 Nov 201117 Nov 2011
Conference number: 18th
https://link.springer.com/book/10.1007/978-3-642-24958-7 (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Neural Information Processing 2011
Abbreviated titleICONIP 2011
Internet address


  • machine learning
  • novelty detection
  • One class classification
  • support vector machine

Cite this