Case-based learning (CBL) algorithms are CBR systems that focus on the topic of learning. This paper notes why CBL algorithms are good choices for many supervised learning tasks, describes a framework for CBL algorithms, outlines a progression of CBL algorithms for tackling learning applications characterized by challenging problems (i.e., noisy cases, poor similarity functions, contextual importance of features), and discusses unsolved problems with the case-based learning approach. Keywords: learning, noise, case retrieval, determining feature importance, determining feature importance in context, evaluation 1 Case-Based Learning This paper concerns a subset of CBR algorithms called case-based learning (CBL) algorithms, which focus on learning issues but do not perform case adaptation, are limited to feature-value case representations, and do not necessarily employ smart indexing schemes for their case base. 1 Nonetheless, CBL systems are well-suited for supervised learning tasks,...
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