Maximal width learning of binary functions

Martin Anthony, Joel Ratsaby

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


This paper concerns learning binary-valued functions defined on R, and investigates how a particular type of 'regularity' of hypotheses can be used to obtain better generalization error bounds. We derive error bounds that depend on the sample width (a notion analogous to that of sample margin for real-valued functions). This motivates learning algorithms that seek to maximize sample width.

Original languageEnglish
Pages (from-to)138-147
Number of pages10
JournalTheoretical Computer Science
Issue number1
StatePublished - 1 Jan 2010


  • Binary function classes
  • Learning algorithms


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