Abstract
The learnability of the class of letter-counts of regular languages (semilinear sets) and other related classes of subsets of N d or Z d With respect to the distribution-free learning model of Valiant (PAC learning model) is characterized. Using the notion of reducibility among learning problems due to Pitt and Warmuth called "prediction preserving reducibility," and a special case thereof, a number of positive and partially negative results are obtained. On the positive side the class of semilinear sets of dimension 1 or 2 is shown to be learnable when the integers are encoded in unary. On the neutral to negative side it is shown that when the integers are encoded in binary the learning problem for semilinear sets as well as for a class of subsets of Z d much simpler than semilinear sets is as hard as learning DNF, a central open problem in the field. A number of hardness results for related learning problems are also given.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
