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dc.contributor.authorBalle Pigem, Borja de
dc.contributor.authorCastro Rabal, Jorge
dc.contributor.authorGavaldà Mestre, Ricard
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.date.accessioned2013-03-13T13:49:57Z
dc.date.created2013-02-18
dc.date.issued2013-02-18
dc.identifier.citationB. Balle; Castro, J.; Gavaldà, R. Learning probabilistic automata : a study in state distinguishability. "Theoretical computer science", 18 Febrer 2013, vol. 473, p. 46-60.
dc.identifier.issn0304-3975
dc.identifier.urihttp://hdl.handle.net/2117/18260
dc.description.abstractKnown 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 in the worst case for every algorithm whose structure resembles existing ones. As a technical tool, a new variant of Statistical Queries termed View the MathML source-queries is defined. We show how to simulate View the MathML source-queries using classical Statistical Queries and show that known PAC algorithms for learning PDFA are in fact statistical query algorithms. Our results include a lower bound: every algorithm to learn PDFA with queries using a reasonable tolerance must make Ω(1/μ1−c) queries for every c>0. Finally, an adaptive algorithm that PAC-learns w.r.t. another measure of complexity is described. This yields better efficiency in many cases, while retaining the same inevitable worst-case behavior. Our algorithm requires fewer input parameters than previously existing ones, and has a better sample bound.
dc.format.extent15 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Sistemes d'informació
dc.subject.lcshDistributed learning
dc.subject.lcshPAC learning
dc.subject.lcshProbabilistic automata
dc.subject.lcshStatistical queries
dc.titleLearning probabilistic automata : a study in state distinguishability
dc.typeArticle
dc.subject.lemacSistemes distribuits
dc.contributor.groupUniversitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge
dc.identifier.doi10.1016/j.tcs.2012.10.009
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0304397512009309
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac11685432
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/216886/EU/Pattern Analysis, Statistical Modelling and Computational Learning 2/PASCAL2
dc.date.lift10000-01-01
local.citation.authorB. Balle; Castro, J.; Gavaldà, R.
local.citation.publicationNameTheoretical computer science
local.citation.volume473
local.citation.startingPage46
local.citation.endingPage60


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