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PAC-Learnability of Probabilistic Deterministic Finite State Automata in Terms of Variation Distance
[chapter]

2005
*
Lecture Notes in Computer Science
*

We consider the problem

doi:10.1007/11564089_14
fatcat:elftebjq3bdxnngqqiidyfktum
*of**PAC*-learning distributions over strings, represented by*probabilistic**deterministic**finite**automata*(PDFAs). ... PDFAs are a*probabilistic*model for the generation*of*strings*of*symbols, that have been used in the context*of*speech and handwriting recognition, and bioinformatics. ... Introduction A*probabilistic**deterministic**finite*automaton (PDFA) is a*deterministic**finite*automaton that has, for each*state*, a probability distribution over the transitions going out from that*state*...##
###
PAC-learnability of probabilistic deterministic finite state automata in terms of variation distance

2007
*
Theoretical Computer Science
*

We consider the problem

doi:10.1016/j.tcs.2007.07.023
fatcat:dsegekwgcfddbljrt4lzqb6zoe
*of**PAC*-learning distributions over strings, represented by*probabilistic**deterministic**finite**automata*(PDFAs). ... PDFAs are a*probabilistic*model for the generation*of*strings*of*symbols, that have been used in the context*of*speech and handwriting recognition, and bioinformatics. ... Introduction A*probabilistic**deterministic**finite*automaton (PDFA) is a*deterministic**finite*automaton that has, for each*state*, a probability distribution over the transitions going out from that*state*...##
###
Page 3464 of Mathematical Reviews Vol. , Issue 99e
[page]

1999
*
Mathematical Reviews
*

*probabilistic*

*finite*

*automata*. ... Summary: “We show that

*deterministic*

*finite*

*automata*(DFAs) with n

*states*and input alphabet © can efficiently be learned from fewer than |Z|n? smallest counterexamples. ...

##
###
Some Classes of Regular Languages Identifiable in the Limit from Positive Data
[chapter]

2002
*
Lecture Notes in Computer Science
*

from Positive Data
p. 63
Learning

doi:10.1007/3-540-45790-9_6
fatcat:nmlknwqoyfbybhb6rpomqrn7qy
*Probabilistic*Residual*Finite**State**Automata*p. 77 Fragmentation: Enhancing Identifiability p. 92 On Limit Points for Some Variants*of*Rigid Lambek Grammars ... Attribute Grammars with Structured Data for Natural Language Processing p. 237 A*PAC**Learnability**of*Simple*Deterministic*Languages p. 249 On the*Learnability**of*Hidden Markov Models p. 261 Shallow ...##
###
Learnability of Probabilistic Automata via Oracles
[chapter]

2005
*
Lecture Notes in Computer Science
*

Efficient

doi:10.1007/11564089_15
fatcat:pmy6zlcgnvfodd76z5gyqtpcci
*learnability*using the*state*merging algorithm is known for a subclass*of**probabilistic**automata*termed µ-distinguishable. ... By combining new results from property testing with the*state*merging algorithm we obtain KL-*PAC**learnability**of*the new*automata*class. ... and the ICT Center*of*Excellence program. ...##
###
Links between probabilistic automata and hidden Markov models: probability distributions, learning models and induction algorithms

2005
*
Pattern Recognition
*

It is proved that

doi:10.1016/j.patcog.2004.03.020
fatcat:uytkvkyfpfbj3lhibry6cfwue4
*probabilistic**deterministic**automata*(PDFA) form a proper subclass*of**probabilistic*non-*deterministic**automata*(PNFA). Two families*of*equivalent models are described next. ... On the other hand, HMMs with final probabilities and*probabilistic**automata*generate distributions over strings*of**finite*length. ... The distinction between*probabilistic**deterministic**automata*(PDFA) and*probabilistic*non-*deterministic**automata*(PNFA) is introduced. ...##
###
Identifiability of Model Properties in Over-Parameterized Model Classes
[chapter]

2013
*
Lecture Notes in Computer Science
*

We exemplify the use

doi:10.1007/978-3-642-40994-3_8
fatcat:7mqtsmxjqnhqleh5b2kb2ifctu
*of*the framework in three different applications: the identification*of*temporal logic properties*of**probabilistic**automata*learned from sequence data, the identification*of*causal ... dependencies in*probabilistic*graphical models, and the transfer*of**probabilistic*relational models to new domains. ... (Non-identifiability*of*PCTL) Let M nd be the class*of*non-*deterministic**probabilistic**finite**automata*, and let D (n) = (Σ * ) n . ...##
###
Learning Stochastic Finite Automata
[chapter]

2004
*
Lecture Notes in Computer Science
*

Stochastic

doi:10.1007/978-3-540-30195-0_16
fatcat:4mlmcctfsvcnnbqnso5v4b6w34
*deterministic**finite**automata*have been introduced and are used in a variety*of*settings. We report here a number*of*results concerning the*learnability**of*these*finite**state*machines. ... In the setting*of*identification in the limit with probability one, we prove that stochastic*deterministic**finite**automata*cannot be identified from only a polynomial quantity*of*data. ... Introduction*Probabilistic**finite**state**automata*[Paz71] have been introduced to describe distributions over strings. ...##
###
Guest Editors' foreword

2013
*
Theoretical Computer Science
*

Moreover, the difficulty

doi:10.1016/j.tcs.2012.10.007
fatcat:ciit7zasgzgytfxx66tzb5onku
*of*learning distributions generated by*probabilistic**deterministic**finite**automata*using statistical queries depends on a parameter µ which is quite frequently studied in the literature ... Learning Theory, 21st International Conference, Gavaldà distinguishes between algorithms that learn*probabilistic**deterministic**finite**automata*from (a) independent and identically distributed samples ...##
###
A bibliographical study of grammatical inference

2005
*
Pattern Recognition
*

The goal

doi:10.1016/j.patcog.2005.01.003
fatcat:62qwskiqcvddjobakbdshwebqq
*of*this paper is to introduce a certain number*of*papers related with grammatical inference. ... The field*of*grammatical inference (also known as grammar induction) is transversal to a number*of*research areas including machine learning, formal language theory, syntactic and structural pattern recognition ... These are*deterministic**finite**automata*with outputs both on the edges and the final*states*. ...##
###
Learning PDFA with Asynchronous Transitions
[chapter]

2010
*
Lecture Notes in Computer Science
*

Our model is rather the

doi:10.1007/978-3-642-15488-1_24
fatcat:mm26lwwm5vbzrdqn3arggxbfle
*finite*-*state*and*deterministic*restriction*of*so-called semi-Markov processes; a widely-studied particular case*of*the latter are continuous-time Markov chains, in which times between ... The problem has also been studied in variants*of*the*PAC*model. ... In particular, the definition*of**probabilistic**deterministic**finite*automaton (PDFA) and associated notation used here are from [2] . ...##
###
On the Learnability of Hidden Markov Models
[chapter]

2002
*
Lecture Notes in Computer Science
*

A simple result is presented that links the learning

doi:10.1007/3-540-45790-9_21
fatcat:7wt7t4gsczc37dxqchsuk6jtz4
*of*hidden Markov models to results in complexity theory about nonlearnability*of**finite**automata*under certain cryptographic assumptions. ... Rather than considering all probability distributions, or even just certain specific ones, the learning*of*a hidden Markov model takes place under a distribution induced by the model itself. ... polynomial size*finite**automata*is not efficiently*pac*-*learnable*. ...##
###
Learning Probability Distributions Generated by Finite-State Machines
[chapter]

2016
*
Topics in Grammatical Inference
*

The methods we review are

doi:10.1007/978-3-662-48395-4_5
fatcat:u4cepbpghjcv7ct6zoqrgir2cy
*state*merging and*state*splitting methods for*probabilistic**deterministic**automata*and the recently developed spectral method for nondeterministic*probabilistic**automata*. ... We review methods for inference*of*probability distributions generated by*probabilistic**automata*and related models for sequence generation. ... We thank the chairs*of*ICGI 2012 for the invitation to present a preliminary version*of*this work as tutorial. We particularly thank the reviewer*of*this version for a thorough and useful work. ...##
###
Editors' Introduction
[chapter]

2013
*
Lecture Notes in Computer Science
*

*state*

*automata*. ... using a polynomial amount

*of*data and processing time, provided that the distributions

*of*the samples are restricted to be generated by one

*of*a large family

*of*related

*probabilistic*

*deterministic*

*finite*... These distributions are generated by

*probabilistic*

*deterministic*

*finite*

*automata*(PDFA). ...

##
###
Learning probabilistic automata and Markov chains via queries

1992
*
Machine Learning
*

*Probabilistic*

*automata*and Markov chains are

*probabilistic*extensions

*of*

*finite*

*state*,

*automata*and have similar structures. ... We investigate the problem

*of*learning

*probabilistic*

*automata*and Markov chains via queries in the teacher-student learning model. ... Part

*of*this paper appeared in the extended abstract "The equivalence and learning

*of*

*probabilistic*

*automata*." Proceedings

*of*the Thirtieth Annual Symposium on Foundations

*of*Computer Science (1989). ...

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