Approximate learning of random subsequential transducers
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In this work, approximate inference of random partial Subsequential Transducers (STs) is addressed. Accessibility and distinguishability of a ST are defined and used to bound the maximum length of samples which are going to form representative sets for target STs. From these representative sets, the sample density required to obtain good approximate STs has been investigated. Dependency of the sample density on the number of states and on the accessibility and distinguishability of the target STs has been evaluated. As a general result, a decrease of the sample density has been found as these parameters increase, suggesting that accessibility and distinguishability are parameters as important as the number of states to evaluate learnability of STs.
KeywordsFinite State Machine Regular Language Minimum Density Effective State Deterministic Finite Automaton
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