de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Impressum Kontakt Einloggen
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Konferenzbeitrag

String Extension Learning Using Lattices

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44814

Kötzing,  Timo
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Kasprzik, A., & Kötzing, T. (2010). String Extension Learning Using Lattices. In C. Martin-Vide, H. Fernau, & A. H. Dediu (Eds.), Language and Automata Theory and Applications (pp. 380-391). Berlin: Springer. doi:10.1007/978-3-642-13089-2_32.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-16F0-E
Zusammenfassung
The class of regular languages is not identifiable from positive data in Gold's language learning model. Many attempts have been made to define interesting classes that \emph{are} learnable in this model, preferably with the associated learner having certain advantageous properties. Heinz '09 presents a set of language classes called \emph{String Extension (Learning) Classes}, and shows it to have several desirable properties. In the present paper, we extend the notion of String Extension Classes by basing it on \emph{lattices} and formally establish further useful properties resulting from this extension. Using lattices enables us to cover a larger range of language classes including the \emph{pattern languages}, as well as to give various ways of \emph{characterizing} String Extension Classes and its learners. We believe this paper to show that String Extension Classes are learnable in a \emph{very natural way}, and thus worthy of further study.