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Empirical Evaluation of a Regular Pattern Inference Algorithm by Minimal Multiple Generalization

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Abstract A regular pattern is a string consisting of constant symbols and mutually distinct variables, and represents the set of the constant strings obtained by substituting possibly empty constant strings fo...r the variables. A learning algorithm, called k-minimal multiple generalization (k-rnmg), finds a minimally general collection of at most k regular patterns that explains all the positive examples. Recently, several attempts have been made at applying this algorithm to protein motif discovery and other knowledge acquisitions. In such applications, its performance is considerably influenced by a class of data and values of learning parameters. This paper empirically evaluates performance of the algorithm k-rnmg on synthesized data to apply it to protein motif discovery.show more

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Created Date 2015.04.14
Modified Date 2020.11.02

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