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Mind Change Complexity of Inferring Unbounded Unions of Pattern Languages from Positive Data

机译:从积极数据中介意改变推断非界定的模式语言的复杂性

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This paper gives a proof that the class of unbounded unions of languages of regular patterns with constant segment length bound is inferable from positive data with mind change bound between ω{sup}ω and ω{sup}(ω{sup}ω). We give a very tight bound on the mind change complexity based on the length of the constant segments and the size of the alphabet of the pattern languages. This is, to the authors' knowledge, the first time a natural class of languages has been shown to be inferable with mind change complexity above ω{sup}ω. The proof uses the notion of closure operators on a class of languages, and also uses the order type of well-partial-orderings to obtain a mind change bound. The inference algorithm presented can be easily applied to a wide range of classes of languages. Finally, we show an interesting connection between proof theory and mind change complexity.
机译:本文给出了一个证明,常规模式的无界工会的类别与恒定段长度绑定的常规模式的语言中的语言中的常规模式可推断出ω{supω{sup}(ω{sup}ω)之间的正常数据。我们基于常量段的长度和模式语言的字母表的长度,在思维变化复杂性的严重束缚。这是对作者的知识,首次被证明是一种自然语言,在ω{sup}ω上方变化复杂性可调整。证据使用封闭运营商的概念在一类语言上,也使用订单类型的井部分排序来获得绑定的思考。所呈现的推理算法可以很容易地应用于各种语言类别。最后,我们在证明理论和心灵变化复杂性之间表现出有趣的联系。

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