A probabilistic approach to case-based inference

作者：Anthony Martin; Ratsaby Joel^*

来源：Theoretical Computer Science, 2015, 589: 61-75.

DOI：10.1016/j.tcs.2015.04.016

• Case based learning
• Multi-category classification
• Generalization error
• Machine learning
• Pattern recognition

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.

• 出版日期2015-7-19