Learning half-spaces on general infinite spaces equipped with a distance function

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Abstract

For a general infinite distance space X, with no assumptions about the distance function, which need not satisfy the metric axioms, it is not clear what the VC-dimension of the class H of half-spaces in X may be and if there are generalization error bounds for learning H. We define a combinatorial dimension of X to be the independence number of the class of balls in X. We compute it for Euclidean space and for several non-metric distance spaces. Using this dimension, we are able to provide a generalization error bound for learning H over any infinite distance space X.

Original languageEnglish
Article number105008
JournalInformation and Computation
Volume291
DOIs
StatePublished - 1 Mar 2023

Keywords

  • Distance space
  • Dual VC-dimension
  • Independence number
  • Learning with large margin (width)
  • Non-metric space
  • Pseudo-dimension
  • Regular n-simplex
  • VC-dimension

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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