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magnanimity

The Anatomy of Biological Interfaces identifies in the limit any total subsequential function. It has been applied over a wide number of machine translation problems with great success. Incorporating the suggestions made in De la Higuera, Vidal and Oncina [dOV96] for automata inference, the DD-OSTIA (Data Driven OSTIA) is presented he

eulogize

CHANT

Jonathan A. N. Fisher,Brian M. Salzbergtic finite automata (sdfa). We deal with the situation arising when wanting to learn sdfa from unrepeated examples. This is intended to model the situation where the data is not generated automatically, but in an order dependent of its probability, as would be the case with the data presented by a h

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Transmembrane Calcium Fluxes and Cell Deathrk with a set of sentences in a language and extract a finite automaton by clustering the states of the trained network. We observe that the generalizations beyond the training set, in the language recognized by the extracted automaton, are due to the training regime: the network performs a “l(fā)oose”

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豐滿中國

Angelo Azzi,Lanfranco Masotti,Arnaldo Veclinduction. This last work has been inspired by the Abbadingo DFA learning competition [14] which took place between Mars and November 1997. SAGE ended up as one of the two winners in that competition. The second winning algorithm, first proposed by Rodney Price, implements a new evidence-driven heuri

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Aline Le Roy,Cécile Breyton,Christine Ebelamples have been developed. Language Understanding can be approached this way as a problem of language . in which the target language is a . language rather than a natural one. Finite-state transducers are used to model the translation process, and are automatically learned from training data consis

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