Case-based Reasoning and the Statistical Challenges

摘要

Case-based reasoning (CBR) solves problems using the already stored knowledge and captures new knowledge, making it immediately available for solving the next problem. Therefore, CBR can be seen as a method for problem solving and also as a method to capture new experience and make it immediately available for problem solving. The CBR paradigm has been originally introduced by the cognitive science community. The CBR community aims at developing computer models that follow this cognitive process. Up to now many successful computer systems have been established on the CBR paradigm for a wide range of real-world problems. In this paper we will review the CBR process and the main topics within the CBR work. Hereby we try bridging between the concepts developed within the CBR community and the statistics community. The CBR topics we describe are similarity, memory organization, CBR learning and case-base maintenance. Then we will review, based on applications, the open problems that need to be solved. The applications we focus on are meta-learning for parameter selection, image interpretation, incremental prototype-based classification and novelty detection and handling. Finally, we summarize our concept on CBR.