A unified model for support vector machine and support vector data description

Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma

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

8 Citations (Scopus)

Abstract

Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments however there is no theoretical work on comparison between these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points to generate a general decision boundary which can be transformed to hyperplane for SVM or hypersphere for SVDD.

Original languageEnglish
Title of host publication2012 International Joint Conference on Neural Networks, IJCNN 2012
DOIs
Publication statusPublished - 2012
Externally publishedYes
EventIEEE International Joint Conference on Neural Networks 2012 - Brisbane Convention & Exhibition Centre, Brisbane, Australia
Duration: 10 Jun 201215 Jun 2012
https://ieeexplore.ieee.org/xpl/conhome/6241467/proceeding (Proceedings)

Conference

ConferenceIEEE International Joint Conference on Neural Networks 2012
Abbreviated titleIJCNN 2012
Country/TerritoryAustralia
CityBrisbane
Period10/06/1215/06/12
Internet address

Keywords

  • Novelty detection
  • one-class classification
  • spherically shaped boundary
  • support vector data description

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