Displaying similar documents to “Learning deterministic regular grammars from stochastic samples in polynomial time”

Learning deterministic regular grammars from stochastic samples in polynomial time

Rafael C. Carrasco, Jose Oncina (2010)

RAIRO - Theoretical Informatics and Applications

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In this paper, the identification of stochastic regular languages is addressed. For this purpose, we propose a class of algorithms which allow for the identification of the structure of the minimal stochastic automaton generating the language. It is shown that the time needed grows only linearly with the size of the sample set and a measure of the complexity of the task is provided. Experimentally, our implementation proves very fast for application purposes.

Note on the complexity of Las Vegas automata problems

Galina Jirásková (2006)

RAIRO - Theoretical Informatics and Applications

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We investigate the complexity of several problems concerning Las Vegas finite automata. Our results are as follows. (1) The membership problem for Las Vegas finite automata is in NL. (2) The nonemptiness and inequivalence problems for Las Vegas finite automata are NL-complete. (3) Constructing for a given Las Vegas finite automaton a minimum state deterministic finite automaton is in NP. These results provide partial answers to some open problems posed by Hromkovič and Schnitger...

Learning extremal regulator implementation by a stochastic automaton and stochastic approximation theory

Ivan Brůha (1980)

Aplikace matematiky

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There exist many different approaches to the investigation of the characteristics of learning system. These approaches use different branches of mathematics and, thus, obtain different results, some of them are too complicated and others do not match the results of practical experiments. This paper presents the modelling of learning systems by means of stochastic automate, mainly one particular model of a learning extremal regulator. The proof of convergence is based on Dvoretzky's Theorem...

Incremental DFA minimisation

Marco Almeida, Nelma Moreira, Rogério Reis (2014)

RAIRO - Theoretical Informatics and Applications - Informatique Théorique et Applications

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We present a new incremental algorithm for minimising deterministic finite automata. It runs in quadratic time for any practical application and may be halted at any point, returning a partially minimised automaton. Hence, the algorithm may be applied to a given automaton at the same time as it is processing a string for acceptance. We also include some experimental comparative results.