A probabilistic approach to case-based inference

Anthony, Martin and Ratsaby, Joel (2015) A probabilistic approach to case-based inference. Theoretical Computer Science, 589. pp. 61-75. ISSN 0304-3975

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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 Hullermeier 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.

Item Type:	Article
Official URL:	http://www.journals.elsevier.com/theoretical-compu...
Additional Information:	© 2015 Elsevier
Divisions:	Mathematics
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited:	23 Apr 2015 10:58
Last Modified:	06 Feb 2024 18:30
URI:	http://eprints.lse.ac.uk/id/eprint/61613

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