Browsing by Author "Balle Pigem, Borja de"
Now showing items 1-13 of 13
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A lower bound for learning distributions generated by probabilistic automata
(Springer, 2010)
Conference report
Open AccessKnown 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
(2008-03-31)
Article
Open AccessMaximal-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
(2014-07)
Article
Restricted access - publisher's policyMarkovian 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
(2009-04-14)
Conference report
Open AccessAbstract-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
(2012)
Conference report
Open AccessMarkovian 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
(Universitat Politècnica de Catalunya, 2009-02)
Master thesis
Open AccessL'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
(Universitat Politècnica de Catalunya, 2013-07-12)
Doctoral thesis
Open AccessThe 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
(Springer, 2010)
Conference lecture
Open AccessIn 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
(2013-02-18)
Article
Restricted access - publisher's policyKnown 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
(2012)
Conference report
Restricted access - publisher's policyIn 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
(2012)
Conference lecture
Open AccessMany 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
(2013-10-07)
Article
Restricted access - publisher's policyIn 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 ... -
Spectral regularization for max-margin sequence tagging
(2014)
Conference report
Open AccessWe frame max-margin learning of latent variable structured prediction models as a convex optimization problem, making use of scoring functions computed by input-output observable operator models. This learning problem can ...
