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書目名稱Grammatical Inference: Algorithms and Applications影響因子(影響力)




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畢業(yè)典禮

https://doi.org/10.1007/978-3-319-59318-0es from a language, a canonical grammar for the training corpus, and a parsing task, Partition Search . Learning constructs a grammar that maximises performance on the parsing task and minimises grammar size. This paper describes Partition Search in detail, also providing theoretical background and

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Rct393

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https://doi.org/10.1007/978-3-531-91531-9e introduced in [.] a class of automata based on properties of residual languages (RFSA) and showed in [.] and [.] that this class could be interesting for grammatical inference purpose. Here, we study properties of 0-reversible languages that can be expressed as properties of their residual languag

intelligible

https://doi.org/10.1007/978-3-322-93584-7mple intrinsic property of the stochastic languages they generate (the set of residual languages is finitely generated by residuals) and that it admits canonical minimal forms. We prove that there are more languages generated by PRFA than by Probabilistic Deterministic Finite Automata (PDFA). We pre

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https://doi.org/10.1007/978-3-0348-5195-4ies. More precisely, rational transducers can be used to implement explicit fragmentation to define new identifiable regular language classes. Context conditions can be used to construe identifiable and characterizable language classes which may contain non-regular languages by means of implicit fra

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,P?dagogische und politische Perspektiven,h trees which are a quite natural way to represent structured information. These trees are used afterwards to infer a stochastic tree automaton, using a well-known grammatical inference algorithm. We propose two extensions of this algorithm: use of sorts and generalization of the infered automaton a