Support vector machine-based image classification for genetic syndrome diagnosis

Amit David, Boaz Lerner

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


We implement structural risk minimization and cross-validation in order to optimize kernel and parameters of a support vector machine (SVM) and multiclass SVM-based image classifiers, thereby enabling the diagnosis of genetic abnormalities. By thresholding the distance of patterns from the hypothesis separating the classes we reject a percentage of the miss-classified patterns reducing the expected risk. Accurate performance of the SVM in comparison to other state-of-the-art classifiers demonstrates the benefit of SVM-based genetic syndrome diagnosis.

Original languageEnglish
Pages (from-to)1029-1038
Number of pages10
JournalPattern Recognition Letters
Issue number8
StatePublished - 1 Jun 2005


  • Fluorescence in situ hybridization (FISH)
  • Genetics
  • Multiclass classification by error correcting output code (ECOC)
  • Rejection
  • Support vector machine (SVM)

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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