Learning a Subclass of Regular Patterns in Polynomial Time

Authors: John Case*, Sanjay Jain**, Rüdiger Reischuk, Frank Stephan and Thomas Zeugmann

Source: Algorithmic Learning Theory, 14th International Conference, ALT 2003, Sapporo, Japan, October 17 - 19, 2003, Proceedings,'' (Ricard Gavaldà, Klaus P. Jantke and Eiji Takimoto, Eds.), Lecture Notes in Artificial Intelligence 2842, pp. 234 - 246, Springer 2003.

Abstract. Presented is an algorithm (for learning a subclass of erasing regular pattern languages) which can be made to run with arbitrarily high probability of success on extended regular languages generated by patterns of the form for unknown m but known c, from number of examples polynomial in m (and exponential in c), where are variables and where are each strings of constants or terminals of length c. This assumes that the algorithm randomly draws samples with natural and plausible assumptions on the distribution.

The more general looking case of extended regular patterns which alternate between a variable and fixed length constant strings, beginning and ending with either a variable or a constant string is similarly handled.

* Supported in part by NSF grant number CCR-0208616 and USDA IFAFS grant number 01-04145.

** Supported in part by NUS grant number R252-000-127-112.

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