A Polynomial Time Learner for a Subclass of Regular Patterns

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

Source: Electronic Colloquium on Computational Complexity, Report TR04-038, April 28, 2004.

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. With the aim of finding a better algorithm, we also explore computer simulations of a heuristic.

* 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.

*** National ICT Australia is funded by the Australian Government's Department of Communications, Information Technology and the Arts and the Australian Research Council through Backing Australia's Ability and the ICT Centre of Excellence Program.

©Copyright 2004 Authors