Ir al contenido (pulsa Retorno)

Universitat Politècnica de Catalunya

    • Català
    • Castellano
    • English
    • LoginRegisterLog in (no UPC users)
  • mailContact Us
  • world English 
    • Català
    • Castellano
    • English
  • userLogin   
      LoginRegisterLog in (no UPC users)

UPCommons. Global access to UPC knowledge

58.848 UPC E-Prints
You are here:
View Item 
  •   DSpace Home
  • E-prints
  • Departaments
  • Departament de Ciències de la Computació
  • Ponències/Comunicacions de congressos
  • View Item
  •   DSpace Home
  • E-prints
  • Departaments
  • Departament de Ciències de la Computació
  • Ponències/Comunicacions de congressos
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

A lower bound for learning distributions generated by probabilistic automata

Thumbnail
View/Open
alt2010final.pdf (181,6Kb)
Share:
 
 
10.1007/978-3-642-16108-7_17
 
  View Usage Statistics
Cita com:
hdl:2117/10556

Show full item record
Balle Pigem, Borja de
Castro Rabal, JorgeMés informacióMés informacióMés informació
Gavaldà Mestre, RicardMés informacióMés informació
Document typeConference report
Defense date2010
PublisherSpringer
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
Known algorithms for learning PDFA can only be shown to run in time polynomial in the so-called distinguishability μ of the target machine, besides the number of states and the usual accuracy and confidence parameters. We show that the dependence on μ is necessary for every algorithm whose structure resembles existing ones. As a technical tool, a new variant of Statistical Queries termed L ∞-queries is defined. We show how these queries can be simulated from samples and observe that known PAC algorithms for learning PDFA can be rewritten to access its target using L∞-queries and standard Statistical Queries. Finally, we show a lower bound: every algorithm to learn PDFA using queries with a resonable tolerance needs a number of queries larger than (1=μ )c for every c < 1.
CitationB. Balle; Castro, J.; Gavaldà, R. A lower bound for learning distributions generated by probabilistic automata. A: International Conference on Algorithmic Learning Theory. "21st International Conference on Algorithmic Learning Theory". Canberra: Springer, 2010, p. 179-193. 
URIhttp://hdl.handle.net/2117/10556
DOI10.1007/978-3-642-16108-7_17
ISBN978-3-642-16107-0
Collections
  • Departament de Ciències de la Computació - Ponències/Comunicacions de congressos [1.219]
  • LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge - Ponències/Comunicacions de congressos [120]
Share:
 
  View Usage Statistics

Show full item record

FilesDescriptionSizeFormatView
alt2010final.pdf181,6KbPDFView/Open

Browse

This CollectionBy Issue DateAuthorsOther contributionsTitlesSubjectsThis repositoryCommunities & CollectionsBy Issue DateAuthorsOther contributionsTitlesSubjects

© UPC Obrir en finestra nova . Servei de Biblioteques, Publicacions i Arxius

info.biblioteques@upc.edu

  • About This Repository
  • Contact Us
  • Send Feedback
  • Inici de la pàgina