A probabilistic approach to case-based inference

Martin Anthony, Joel Ratsaby

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The central problem in case based reasoning (CBR) is to infer a solution for a new problem-instance by using a collection of existing problem-solution cases. The basic heuristic guiding CBR is the hypothesis that similar problems have similar solutions. Recently, some attempts at formalizing CBR in a theoretical framework have been made, including work by Hüllermeier who established a link between CBR and the probably approximately correct (PAC) theoretical model of learning in his 'case-based inference' (CBI) formulation. In this paper we develop further such probabilistic modelling, framing CBI it as a multi-category classification problem. We use a recently-developed notion of geometric margin of classification to obtain generalization error bounds.

Original languageEnglish
Pages (from-to)61-75
Number of pages15
JournalTheoretical Computer Science
Volume589
DOIs
StatePublished - 19 Jul 2015
Externally publishedYes

Keywords

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

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