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Maximal-margin case-based inference

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. Institute of Electrical and Electronics Engineers, New York, USA, pp. 112-119. ISBN 9781479915682 (Submitted)

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Identification Number: 10.1109/UKCI.2013.6651295


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:
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: 25 Nov 2021 12:45

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