Ara es mostren els items 1-12 de 12

  • A lower bound for learning distributions generated by probabilistic automata 

    Balle Pigem, Borja de; Castro Rabal, Jorge; Gavaldà Mestre, Ricard (Springer, 2010)
    Text en actes de congrés
    Accés obert
    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. ...
  • Absolute-type shaft encoding using LFSR sequences with a prescribed length 

    Fuertes Armengol, José Mª; Balle Pigem, Borja de; Ventura Capell, Enric (2008-03-31)
    Article
    Accés obert
    Maximal-length binary sequences have existed for a long time. They have many interesting properties, and one of them is that, when taken in blocks of n consecutive positions, they form 2n - 1 different codes in a closed ...
  • Adaptively learning probabilistic deterministic automata from data streams 

    Balle Pigem, Borja de; Castro Rabal, Jorge; Gavaldà Mestre, Ricard (2014-07)
    Article
    Accés restringit per política de l'editorial
    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 ...
  • An algorithm to design prescribed length codes for single-tracked shaft encoders 

    Balle Pigem, Borja de; Ventura Capell, Enric; Fuertes Armengol, José Mª (2009-04-14)
    Text en actes de congrés
    Accés obert
    Abstract-Maximal-length binary shift register sequences have been known for a long time. They have many interesting properties, one of them is that when taken in blocks of n consecutive positions they form 2n - 1 different ...
  • Bootstrapping and learning PDFA in data streams 

    Balle Pigem, Borja de; Castro Rabal, Jorge; Gavaldà Mestre, Ricard (2012)
    Text en actes de congrés
    Accés obert
    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 ...
  • Extensions de l'algorisme clàssic de Whitehead 

    Balle Pigem, Borja de (Universitat Politècnica de Catalunya, 2009-02)
    Projecte Final de Màster Oficial
    Accés obert
    L'algoritme clàssic de Whitehead decideix si dues paraules del grup lliure pertanyen o no a la mateixa òrbita per l'acció del grup d'automorfismes. La demostració clàssica és molt combinatòrica i tècnica (i desagradable ...
  • Learning finite-state machines: statistical and algorithmic aspects 

    Balle Pigem, Borja de (Universitat Politècnica de Catalunya, 2013-07-12)
    Tesi
    Accés obert
    The present thesis addresses several machine learning problems on generative and predictive models on sequential data. All the models considered have in common that they can be de ned in terms of nite-state machines. On ...
  • 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 ...
  • Spectral learning in non-deterministic dependency parsing 

    Luque, Franco M.; Quattoni, Ariadna Julieta; Balle Pigem, Borja de; Carreras Pérez, Xavier (2012)
    Text en actes de congrés
    Accés restringit per política de l'editorial
    In this paper we study spectral learning methods for non-deterministic split head-automata grammars, a powerful hidden-state formalism for dependency parsing. We present a learning algorithm that, like other spectral ...
  • Spectral learning of general weighted automata via constrained matrix completion 

    Balle Pigem, Borja de; Mohri, Mehryar (2012)
    Comunicació de congrés
    Accés obert
    Many tasks in text and speech processing and computational biology require estimating functions mapping strings to real numbers. A broad class of such functions can be defined by weighted automata. Spectral methods based ...
  • Spectral learning of weighted automata: a forward-backward perspective 

    Balle Pigem, Borja de; Carreras Pérez, Xavier; Luque, Franco M.; Quattoni, Ariadna Julieta (2013-10-07)
    Article
    Accés restringit per política de l'editorial
    In recent years we have seen the development of efficient provably correct algorithms for learning Weighted Finite Automata (WFA). Most of these algorithms avoid the known hardness results by defining parameters beyond the ...