Multi-category classifiers and sample width

Martin Anthony, Joel Ratsaby

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


In a recent paper, the authors introduced the notion of sample width for binary classifiers defined on the set of real numbers. It was shown that the performance of such classifiers could be quantified in terms of this sample width. This paper considers how to adapt the idea of sample width so that it can be applied in cases where the classifiers are multi-category and are defined on some arbitrary metric space.

Original languageEnglish
Pages (from-to)1223-1231
Number of pages9
JournalJournal of Computer and System Sciences
Issue number8
StatePublished - 1 Dec 2016


  • Generalization error
  • Machine learning
  • Multi-category classification
  • Pattern recognition


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