在有限的正性信息和大量记忆变化中学习语言
Learning Languages in the Limit from Positive Information with Finitely Many Memory Changes
摘要：
我们研究学习集合的语言从文本的归纳推理机访问当前的数据和有界的记忆形式的状态。这样的有界记忆状态（BMS）学习器被认为是成功的情况下，它最终解决了一个正确的假设，同时只利用许多不同的状态。我们给出了所有成对关系的完整映射，以建立成功学习的标准集合。最突出的是，我们表明，非U形是没有限制的，而保守性和（强）单调性。一些结果结转从迭代学习的一般引理表明，丰富的限制（语义限制），迭代和有界记忆状态学习是等价的。我们还给出了一个非语义限制（强非U形）的例子，其中两个设置不同。
英文摘要：
We investigate learning collections of languages from texts by an inductive inference machine with access to the current datum and a bounded memory in form of states. Such a bounded memory states (BMS) learner is considered successful in case it eventually settles on a correct hypothesis while exploiting only finitely many different states. We give the complete map of all pairwise relations for an established collection of criteria of successfull learning. Most prominently, we show that non-U-shapedness is not restrictive, while conservativeness and (strong) monotonicity are. Some results carry over from iterative learning by a general lemma showing that, for a wealth of restrictions (the semantic restrictions), iterative and bounded memory states learning are equivalent. We also give an example of a non-semantic restriction (strongly non-U-shapedness) where the two settings differ.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
