Dual margin approach on a Lagrangian support vector machine

Jae Pil Hwang, Seongkeun Park, Euntai Kim

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


In this paper, we propose a new support vector machine (SVM) called dual margin Lagrangian support vectors machine (DMLSVM). Unlike other SVMs which use only support vectors to determine the separating hyperplanes, DMLSVM utilizes all the available training data for training the classifier, thus producing robust performance. The training data are weighted differently depending on whether they are in a marginal region or surplus region. For fast training, DMLSVM borrows its training algorithm from Lagrangian SVM (LSVM) and tailors the algorithm to its formulation. The convergence of our training method is rigorously proven and its validity is tested on a synthetic test set and UCI dataset. The proposed method can be used in a variety of applications such as a recommender systems for web contents of IPTV services.

Original languageEnglish
Pages (from-to)695-708
Number of pages14
JournalInternational Journal of Computer Mathematics
Issue number4
Publication statusPublished - 2011 Mar

Bibliographical note

Funding Information:
This work was supported by ‘Method of creation and dynamically associated with contents on the web for IPTV services’ of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Ministry of Knowledge Economy.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics


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