Anthony, Martin and Ratsaby, Joel (2013) Maximal-margin case-based inference. In: Jin, Yaochu and Thomas, Spencer Angus, (eds.) 2013 13th Uk Workshop on Computational Intelligence (Ukci): Management School Foyer, University of Surrey, Guildford, Surrey, Uk. IEEE Conference Publications. IEEE, New York, USA, pp. 112-119. ISBN 9781479915682 (Submitted)
Full text not available from this repository.Abstract
The central problem in case-based reasoning (CBR) is to produce a solution for a new problem instance by using a set of existing problem-solution cases. The basic heuristic guiding CBR is the assumption that similar problems have similar solutions. CBR has been often criticized for lacking a sound theoretical basis, and there has only recently been some attempts at developing a theoretical framework, including recent work by Hullermeier, who made a link between CBR and the probably approximately correct (or PAC) probabilistic model of learning in his `case-based inference' (CBI) formulation. In this paper we present a new framework of CBI which models 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: | Book Section |
|---|---|
| Official URL: | http://ieeexplore.ieee.org/Xplore/home.jsp |
| Additional Information: | © 2013 IEEE |
| Divisions: | Mathematics |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Date Deposited: | 02 Oct 2013 13:59 |
| Last Modified: | 02 Jan 2024 07:45 |
| URI: | http://eprints.lse.ac.uk/id/eprint/53195 |
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