Increasing efficiency of SVM by adaptively penalizing outliers

Yiqiang Zhan, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, a novel training method is proposed to increase the classification efficiency of support vector machine (SVM). The efficiency of the SVM is determined by the number of support vectors, which is usually large for representing a highly convoluted separation hypersurface. We noted that the separation hypersurface is made unnecessarily over-convoluted around extreme outliers, which dominate the objective function of SVM. To suppress the domination from extreme outliers and thus relatively simplify the shape of separation hypersurface, we propose a method of adaptively penalizing the outliers in the objective function. Since our reformulated objective function has the similar format of the standard SVM, the idea of the existing SVM training algorithms is borrowed for training the proposed SVM. Our proposed method has been tested on the UCI machine learning repository, as well as a real clinical problem, i.e., tissue classification in prostate ultrasound images. Experimental results show that our method is able to dramatically increase the classification efficiency of the SVM, without losing its generalization ability.

Original languageEnglish
Title of host publicationEnergy Minimization Methods in Computer Vision and Pattern Recognition - 5th International Workshop, EMMCVPR 2005, Proceedings
Pages539-551
Number of pages13
DOIs
Publication statusPublished - 2005
Event5th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2005 - St. Augustine, FL, United States
Duration: 2005 Nov 92005 Nov 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3757 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2005
CountryUnited States
CitySt. Augustine, FL
Period05/11/905/11/11

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Zhan, Y., & Shen, D. (2005). Increasing efficiency of SVM by adaptively penalizing outliers. In Energy Minimization Methods in Computer Vision and Pattern Recognition - 5th International Workshop, EMMCVPR 2005, Proceedings (pp. 539-551). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3757 LNCS). https://doi.org/10.1007/11585978_35