Inductive identification of pattern languages restricted substitutions
COLT '90 Proceedings of the third annual workshop on Computational learning theory
A polynomial-time algorithm for learning k-variable pattern languages from examples
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Identification of unions of languages drawn from an identifiable class
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Incremental learning from positive data
Journal of Computer and System Sciences
An average-case optimal one-variable pattern language learner
Journal of Computer and System Sciences - Eleventh annual conference on computational learning theory&slash;Twelfth Annual IEEE conference on computational complexity
Stochastic Finite Learning of the Pattern Languages
Machine Learning
Polynomial Time Inference of Extended Regular Pattern Languages
Proceedings of RIMS Symposium on Software Science and Engineering
Typed pattern languages and their learnability
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Learning indexed families of recursive languages from positive data: A survey
Theoretical Computer Science
Discontinuities in pattern inference
Theoretical Computer Science
Polynomial-Time algorithms for learning typed pattern languages
LATA'12 Proceedings of the 6th international conference on Language and Automata Theory and Applications
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Patterns provide a simple, yet powerful means of describing formal languages. However, for many applications, neither patterns nor their generalized versions of typed patterns are expressive enough. This paper extends the model of (typed) patterns by allowing relations between the variables in a pattern. The resulting formal languages are called Relational Pattern Languages (RPLs). We study the problem of learning RPLs from positive data (text) as well as the membership problem for RPLs. These problems are not solvable or not efficiently solvable in general, but we prove positive results for interesting subproblems. We further introduce a new model of learning from a restricted pool of potential texts. Probabilistic assumptions on the process that generates words from patterns make the appearance of some words in the text more likely than that of other words. We prove that, in our new model, a large subclass of RPLs can be learned with high confidence, by effectively restricting the set of likely candidate patterns to a finite set after processing a single positive example.