Identification of unions of languages drawn from an identifiable class
COLT '89 Proceedings of the second annual workshop on Computational learning theory
The correct definition of finite elasticity: corrigendum to identification of unions
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Maximal common subsequences and minimal common supersequences
Information and Computation
The Complexity of Some Problems on Subsequences and Supersequences
Journal of the ACM (JACM)
Inference of Reversible Languages
Journal of the ACM (JACM)
Polynomial Time Inference of Extended Regular Pattern Languages
Proceedings of RIMS Symposium on Software Science and Engineering
STACS '94 Proceedings of the 11th Annual Symposium on Theoretical Aspects of Computer Science
Inductive Inference of Unbounded Unions of Pattern Languages from Positive Data
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
Characteristic Sets for Unions of Regular Pattern Languages and Compactness
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
Learning of erasing primitive formal systems from positive examples
Theoretical Computer Science - Algorithmic learning theory
Developments from enquiries into the learnability of the pattern languages from positive data
Theoretical Computer Science
Inferring unions of the pattern languages by the most fitting covers
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Best fitting fixed-length substring patterns for a set of strings
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
Measuring over-generalization in the minimal multiple generalizations of biosequences
DS'05 Proceedings of the 8th international conference on Discovery Science
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A regular pattern is a string of constant symbols and distinct variables. A semantics of a set P of regular patterns is a union L(P) of erasing pattern languages generated by patterns in P. The paper deals with the class RPk of sets of at most k regular patterns, and an efficient learning from positive examples of the language class defined by RPk. In efficient learning languages, the complexity for the MINL problem to find one of minimal languages containing a given sample is one of very important keys. Arimura et al.[5] introduced a notion of compactness w.r.t. containment for more general framework, called generalization systems, than RPk of language description which guarantees the equivalency between the semantic containment L(P) 驴 L(Q) and the syntactic containment P 驴 Q, where 驴 is a syntactic subsumption over the generalization systems.Under the compactness, the MINL problem reduces to finding one of minimal sets in RPk for a given sample under the subsumption 驴. They gave an efficient algorithm to find such minimal sets under the assumption of compactness and some conditions.We first show that for each k 驴 1, the class RPk has compactness if and only if the number of constant symbols is greater than k+1. Moreover, we prove that for each P 驴 RPk, a finite subset S2(P) is a characteristic set of L(P) within the class, where S2(P) consists of strings obtained from P by substituting strings with length two for each variable. Then our class RPk is shown to be polynomial time inferable from positive examples using the efficient algorithm of the MINL problem due to Arimura et al.[5], provided the number of constant symbols is greater than k + 1.