Learning probabilistic automata using residuals

DOI10.1007/978-3-030-85315-0_17OpenAlexW3194347685MaRDI QIDQ2119983

Marcello M. Bonsangue, Shuo Chen, Wenjing Chu

Publication date: 31 March 2022

Full work available at URL: https://hdl.handle.net/1887/3257031

zbMATH Keywords

learning automataprobabilistic automata\(L_2\) distance between discrete distributionsresidual finite state automata

Mathematics Subject Classification ID

Theory of computing (68Qxx)

Uses Software

• RFSA

Cites Work

• Grammatical inference and applications. 2nd international colloquium, ICGI-94, Alicante, Spain, September 21-23, 1994. Proceedings
• Characteristic sets for polynomial grammatical inference
• The consensus string problem and the complexity of comparing hidden Markov models.
• \(L^\ast\)-based learning of Markov decision processes (extended version)
• Links between probabilistic automata and hidden Markov models: probability distributions, learning models and induction algorithms
• L_p DISTANCE AND EQUIVALENCE OF PROBABILISTIC AUTOMATA
• A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
• Algorithmic Learning Theory
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