Hyper-Minimization for Deterministic Weighted Tree Automata

Maletti, Andreas; Quernheim, Daniel

doi:10.4204/eptcs.151.22

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…This constraint is rather simplistic, but it allows a convenient theoretical treatment [16] and efficient minimization algorithms [17][18][19][20]. Besides there are models for which finitely many errors are absolutely inconsequential [21].…”

Section: Introductionmentioning

confidence: 99%

Hyper-optimization for deterministic tree automata

Maletti

2015

Theoretical Computer Science

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DTA = deterministic tree automaton DBA / DCA = deterministic BÜCHI / Co-BÜCHI automaton Deterministic Tree Automaton Definition (GÉCSEG, STEINBY 1984) (Q, Σ, δ, F ) deterministic tree automaton (DTA) Q finite set states Σ ranked alphabet input symbolsDeterministic Tree Automaton Definition (GÉCSEG, STEINBY 1984) (Q, Σ, δ, F ) deterministic tree automaton (DTA) Q finite set states Σ ranked alphabet input symbolsDeterministic Tree Automaton Example states q α , q β (nonfinal) and q γ , q σ (final) nullary input symbols α, β, γ and binary σ for all nullary symbols π, πDeterministic Tree Automaton Example states q α , q β (nonfinal) and q γ , q σ (final) nullary input symbols α, β, γ and binary σ for all nullary symbols π, πDeterministic Tree Automaton Example states q α , q β (nonfinal) and q γ , q σ (final) nullary input symbols α, β, γ and binary σ for all nullary symbols π, πDeterministic Tree Automaton Example states q α , q β (nonfinal) and q γ , q σ (final) nullary input symbols α, β, γ and binary σ for all nullary symbols π, πDeterministic Tree Automaton Example states q α , q β (nonfinal) and q γ , q σ (final) nullary input symbols α, β, γ and binary σ for all nullary symbols π, πDeterministic Tree Automaton Example states q α , q β (nonfinal) and q γ , q σ (final) nullary input symbols α, β, γ and binary σ for all nullary symbols π, πDeterministic Tree Automaton Definition (Recognized tree language)

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Section: Introductionmentioning

confidence: 99%

Hyper-optimization for deterministic tree automata

Maletti

2015

Theoretical Computer Science

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“…Since hyper-minimization trivially reduces to minimization [8], faster hyper-minimization algorithms would imply faster minimization algorithms, which have remained elusive. Hyper-minimization was already generalized to weighted dfa [13] and to dfa over innite strings [15]. An overview of existing hyper-minimization algorithms can be found in [12].…”

Section: Introductionmentioning

confidence: 99%

Hyper-Minimization for Deterministic Tree Automata

Jeż

Maletti

2013

Int. J. Found. Comput. Sci.

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Abstract. Hyper-minimization aims to reduce the size of the representation of a language beyond the limits imposed by classical minimization. To this end, the hyper-minimal representation can represent a language that has a nite dierence to the original language. The rst hyperminimization algorithm is presented for (bottom-up) deterministic tree automata, which represent the recognizable tree languages. It runs in time O( mn), where is the maximal rank of the input symbols, m is the number of transitions, and n is the number of states of the input tree automaton. We generalize hyper-minimization to deterministic tree automata (dta) [5,6], which have applications in XML processing [10] and natural language processing [11]. We faithfully generalize the existing denitions from dfa to dta.

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Hyper-Minimization for Deterministic Weighted Tree Automata

Cited by 2 publications

References 24 publications

Hyper-optimization for deterministic tree automata

Hyper-optimization for deterministic tree automata

Hyper-Minimization for Deterministic Tree Automata

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