کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
437721 | 690179 | 2015 | 15 صفحه PDF | دانلود رایگان |
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 Hüllermeier 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.
Journal: Theoretical Computer Science - Volume 589, 19 July 2015, Pages 61–75