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Case-based representation and learning of pattern languages
1995
Theoretical Computer Science
Pattern languages seem to suit case-based reasoning particularly well. Therefore, the problem of inductively learning pattern languages is paraphrased in a case-based manner. A careful investigation requires a formal semantics for case bases together with similarity measures in terms of formal languages. Two basic semantics are introduced and investigated. It turns out that representability problems are major obstacles for case-based learnability. Restricting the attention to the so-called
doi:10.1016/0304-3975(95)91134-c
fatcat:uqtktchsijdltdayludeieuevm