Anthony, Martin 
ORCID: 0000-0002-7796-6044 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: | 01 Nov 2024 04:24 |
| URI: | http://eprints.lse.ac.uk/id/eprint/61613 |
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