Large width nearest prototype classification on general distance spaces

Martin Anthony, Joel Ratsaby

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In this paper we consider the problem of learning nearest-prototype classifiers in any finite distance space; that is, in any finite set equipped with a distance function. An important advantage of a distance space over a metric space is that the triangle inequality need not be satisfied, which makes our results potentially very useful in practice. We consider a family of binary classifiers for learning nearest-prototype classification on distance spaces, building on the concept of large-width learning which we introduced and studied in earlier works. Nearest-prototype is a more general version of the ubiquitous nearest-neighbor classifier: a prototype may or may not be a sample point. One advantage in the approach taken in this paper is that the error bounds depend on a ‘width’ parameter, which can be sample-dependent and thereby yield a tighter bound.

Original languageEnglish
Pages (from-to)65-79
Number of pages15
JournalTheoretical Computer Science
Volume738
DOIs
StatePublished - 22 Aug 2018
Externally publishedYes

Keywords

  • Distance space
  • LVQ
  • Large margin learning
  • Metric space
  • Nearest-neighbor classification

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