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Large margin case-based reasoning

Anthony, Martin and Ratsaby, Joel (2013) Large margin case-based reasoning. RUTCOR Research Reports (RRR 2-2013). Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA.

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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. CBR has been often criticized for lacking a sound theoretical basis, and there has only recently been some attempts at formalizing CBR in a theoretical framework, including work by Hullermeier who made the link between CBR and the probably approximately correct (PAC) theoretical 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: Monograph (Report)
Official URL:
Additional Information: © 2013 Rutgers, The State University of New Jersey
Divisions: Mathematics
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited: 04 Mar 2013 16:24
Last Modified: 16 May 2024 13:21
Funders: ST Programme of the European Community, under the PASCAL2 Network of Excellence, IST-2007-216886

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