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dc.contributor.authorCastro Rabal, Jorge
dc.contributor.authorGavaldà Mestre, Ricard
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.identifier.citationCastro, J., Gavaldà, R. Learning probability distributions generated by finite-state machines. A: "Topics in grammatical inference". Berlín: Springer, 2016, p. 113-142.
dc.description.abstractWe review methods for inference of probability distributions generated by probabilistic automata and related models for sequence generation. We focus on methods that can be proved to learn in the inference in the limit and PAC formal models. The methods we review are state merging and state splitting methods for probabilistic deterministic automata and the recently developed spectral method for nondeterministic probabilistic automata. In both cases, we derive them from a high-level algorithm described in terms of the Hankel matrix of the distribution to be learned, given as an oracle, and then describe how to adapt that algorithm to account for the error introduced by a finite sample.
dc.format.extent30 p.
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.lcshMachine theory
dc.subject.otherProbabilistic automata
dc.subject.otherPAC formal models
dc.subject.otherFinite-state machines
dc.titleLearning probability distributions generated by finite-state machines
dc.typePart of book or chapter of book
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacMàquines, Teoria de
dc.contributor.groupUniversitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
local.citation.authorCastro, J.; Gavaldà, R.
local.citation.publicationNameTopics in grammatical inference

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