@inproceedings{41fcf4aca1a24a36b7d1b8fae94baff6,

title = "Learning DFA from simple examples",

abstract = "We present a framework for learning DFA from simple examples. We show that efficient PAC learning of DFA is possible if the class of distributions is restricted to simple distributions where a teacher might choose examples based on the knowledge of the target concept. This answers an interesting variant of an open research question posed in Pitt's seminal paper: Are DFA's PAC-identifiable if examples are drawn from the uniform distribution, or some other known simple distribution? Our approach uses the RPNI algorithm for learning DFA from labeled exampies. In particular, we describe an efficient learning algorithm for exact learning of the target DFA with high probability when a bound on the number of states (N) of the target DFA is known in advance. When N is not known, we show how this algorithm can be used for efficient PAC learning of DFAs.",

author = "Rajesh Parekh and Vasant Honavar",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1997. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.; 8th International Workshop on Algorithmic Learning Theory, ALT 1997 ; Conference date: 06-10-1997 Through 08-10-1997",

year = "1997",

doi = "10.1007/3-540-63577-7_39",

language = "English (US)",

isbn = "3540635777",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "116--131",

editor = "Ming Li and Akira Maruoka",

booktitle = "Algorithmic Learning Theory - 8th International Workshop, ALT 1997, Proceedings",

address = "Germany",

}