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Iterated Transductions and Efficient Learning from Positive Data: A Unifying View,ficiently learnable in the limit from positive data. Furthermore, the set contains the class of .-reversible languages and the class of .-locally testable languages in the strict sense just as example language classes. This paper also proposes a framework for defining language classes based on itera

微粒

共棲

Combination of Estimation Algorithms and Grammatical Inference Techniques to Learn Stochastic Conteaximize a certain criterion function from a training sample by using gradient descendent techniques. In this optimization process, the obtaining of the initial SCFGs is an important factor, given that it affects the convergence process and the maximum which can be achieved. Here, we show experimenta

車床

On the Relationship between Models for Learning in Helpful Environments,nd in the PAC learning framework (concept classes such as . (DFA) are not efficiently learnable in the PAC model). The PAC model’s requirement of learnability under all conceivable distributions could be considered too stringent a restriction for practical applications. Several models for learning i

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organism

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https://doi.org/10.1007/978-1-349-16692-3s of internal contextual languages, namely, k-uniform and strictly internal contextual languages which are incomparable classes and provide an algorithm to learn these classes. The algorithm can be used when the rules are applied in a parallel mode.