A hybrid classifier based on boxes and nearest neighbors

Martin Anthony, Joel Ratsaby

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


In this paper we analyse the generalization performance of a type of binary classifier defined on the unit cube. This classifier combines some of the aspects of the standard methods that have been used in the logical analysis of data (LAD) and geometric classifiers, with a nearest-neighbor paradigm. We assess the predictive performance of the new classifier in learning from a sample, obtaining generalization error bounds that improve as a measure of 'robustness' of the classifier on the training sample increases.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalDiscrete Applied Mathematics
StatePublished - 31 Jul 2014


  • Generalization error
  • LAD methods
  • Large margin learning
  • Learning algorithms
  • Logical analysis of data
  • Machine learning


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