Now showing items 1-5 of 5

  • A lower bound for learning distributions generated by probabilistic automata 

    Balle Pigem, Borja de; Castro Rabal, Jorge; Gavaldà Mestre, Ricard (Springer, 2010)
    Conference report
    Open Access
    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. ...
  • Adaptively learning probabilistic deterministic automata from data streams 

    Balle Pigem, Borja de; Castro Rabal, Jorge; Gavaldà Mestre, Ricard (2014-07)
    Article
    Restricted access - publisher's policy
    Markovian models with hidden state are widely-used formalisms for modeling sequential phenomena. Learnability of these models has been well studied when the sample is given in batch mode, and algorithms with PAC-like ...
  • Learning PDFA with asynchronous transitions 

    Balle Pigem, Borja de; Castro Rabal, Jorge; Gavaldà Mestre, Ricard (Springer, 2010)
    Conference lecture
    Open Access
    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
    Restricted access - publisher's policy
    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 ...
  • Simple PAC learning of simple decision lists 

    Castro Rabal, Jorge; Balcázar Navarro, José Luis (1995-06)
    External research report
    Open Access
    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 ...