|Title||Usages of Generalization in CBR|
|Publication Type||Conference Paper|
|Year of Publication||2007|
|Editor||Weber R.O., Richter M.M|
|Conference Name||Lecture Notes in Artificial Intelligence|
The aim of this paper is to analyze how the generalizations built by a CBR method can be used as local approximations of a concept. From this point of view, these local approximations can take a role similar to the global approximations built by eager learning methods. Thus, we propose that local approximations can be interpreted either as: 1) a symbolic similitude among a set of cases, 2) a partial domain model, or 3) an explanation of the system classification. We illustrate these usages by solving the Predictive Toxicology task.
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