Exploració per tema "PAC learning"

Ara es mostren els items 3-7 de 7

    • Learning PDFA with asynchronous transitions 

      Balle Pigem, Borja de; Castro Rabal, Jorge; Gavaldà Mestre, Ricard (Springer, 2010)
      Comunicació de congrés
      Accés obert
      In this paper we extend the PAC learning algorithm due to Clark and Thollard for learning distributions generated by PDFA to automata whose transitions may take varying time lengths, governed by exponential distributions.

    • Learning probabilistic automata : a study in state distinguishability 

      Balle Pigem, Borja de; Castro Rabal, Jorge; Gavaldà Mestre, Ricard (2013-02-18)
      Article
      Accés restringit per política de l'editorial
      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 ...

    • Note: More efficient conversion of equivalence-query algorithms to PAC algorithms 

      Gavaldà Mestre, Ricard (2009-05)
      Report de recerca
      Accés obert
      We present a method for transforming an Equivalence-query algorithm using Q queries into a PAC-algorithm using Q/epsilon + O( (Q^(2/3) / epsilon ) * log(Q / delta) examples in expectation. The method is a variation of that ...

    • Query, PACS and simple-PAC learning 

      Castro Rabal, Jorge; Guijarro Guillem, David (1998-01)
      Report de recerca
      Accés obert
      We study a distribution dependent form of PAC learning that uses probability distributions related to Kolmogorov complexity. We relate the PACS model, defined by Denis, D'Halluin and Gilleron, with the standard simple-PAC ...

    • Simple PAC learning of simple decision lists 

      Castro Rabal, Jorge; Balcázar Navarro, José Luis (1995-06)
      Report de recerca
      Accés obert
      We prove that log n-decision lists - the class of decision lists such that all their terms have low Kolmogorov complexity - are learnable in the simple PAC learning model. The proof is based on a transformation from an ...