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The skstrings method for inferring PFSA
 In Proceedings of the
, 1997
"... We describe a simple, fast and easy to implement recursive algorithm with four alternate intuitive heuristics for inferring Probabilistic Finite State Automata. The algorithm is an extension for stochastic machines of the ktails method introduced in 1972 by Biermann and Feldman for nonstochastic m ..."
Abstract

Cited by 31 (2 self)
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We describe a simple, fast and easy to implement recursive algorithm with four alternate intuitive heuristics for inferring Probabilistic Finite State Automata. The algorithm is an extension for stochastic machines of the ktails method introduced in 1972 by Biermann and Feldman for nonstochastic machines. Experiments comparing the two are done and benchmark results are also presented. It is also shown that skstrings performs better than ktails at least in inferring small automata. Introduction When given a finite number of examples of the behaviour of a probabilistic state determined machine, it is possible to imagine methods by which we can infer its structure. Ideally, we would like to identify the exact automaton which generated the strings. But it is impossible to do this from the behaviour of the machine because more than one nonminimal machine may generate the same language. This paper is concerned not with identifing the generating machine, which is demonstratably impossib...
PFSA Modelling of Behavioural Sequences by Evolutionary Programming
"... Behavioural observations can often be described as a sequence of symbols drawn from a finite alphabet. However the inductive inference of such strings by any automated technique to produce models of the data is a nontrivial task. This paper considers modelling of behavioural data using probabilisti ..."
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Cited by 2 (1 self)
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Behavioural observations can often be described as a sequence of symbols drawn from a finite alphabet. However the inductive inference of such strings by any automated technique to produce models of the data is a nontrivial task. This paper considers modelling of behavioural data using probabilistic finite state automata (PFSAs). There are a number of informationtheoretic techniques for evaluating possible hypotheses. The measure used in this paper is the Minimum Message Length (MML) of Wallace. Although attempts have been made to construct PFSA models by incremental addition of substrings using heuristic rules and the MML to give the lowest information cost, the resultant models cannot be shown to be globally optimal. Fogel's Evolutionary Programming can produce globally optimal PFSA models by evolving data structures of arbitrary complexity without the requirement to encode the PFSA into binary strings as in Genetic Algorithms. However, evaluation of PFSAs during the evolution pro...
A Complexity Measure for Diachronic Chinese Phonology
 In Proceedings of the SIGPHON97 workshop on computational linguistics at the ACL'97/EACL'97 joint conference
, 1997
"... This paper addresses the problem of deriving distance measures between parent and daughter languages with specific relevance to historical Chinese phonology. The diachronic relationship between the languages is modelled as a Probabilistic Fi nite State Automaton. The Minimum Mes sage Length ..."
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This paper addresses the problem of deriving distance measures between parent and daughter languages with specific relevance to historical Chinese phonology. The diachronic relationship between the languages is modelled as a Probabilistic Fi nite State Automaton. The Minimum Mes sage Length principle is then employed to find the complexity of this structure. The idea is that this measure is representative of the amount of dissimilarity between the two languages.
A Genetic Algorithm for Regular Inference
, 2001
"... We show how a genetic algorithm can be used for the inference of a regular language from a set of positive (and optionally also negative) examples. The genetic algorithm attempts to find the simplest description of the example data in terms of a finite state automaton model. ..."
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We show how a genetic algorithm can be used for the inference of a regular language from a set of positive (and optionally also negative) examples. The genetic algorithm attempts to find the simplest description of the example data in terms of a finite state automaton model.