论文标题
学习可索引课程的地图
Maps for Learning Indexable Classes
论文作者
论文摘要
我们从积极数据中研究了索引家庭的学习，学习者可以自由选择一个至少包括要学习的语言的假设空间（具有统一的成员资格）。这摘要一项非常普遍的学习任务，可以在许多领域中找到，例如学习普通语言或自然语言的学习。我们对学习的各种限制感兴趣，例如一致性，保守性或设定驱动性，体现了各种自然学习限制。 基于文献的先前结果，我们提供了各种学习标准的几个地图（所有成对关系的描述），包括单调性限制的地图和类似的标准，以及对数据显示的限制的地图。此外，对于各种学习标准，我们认为学习者是否可以保持一致。
We study learning of indexed families from positive data where a learner can freely choose a hypothesis space (with uniformly decidable membership) comprising at least the languages to be learned. This abstracts a very universal learning task which can be found in many areas, for example learning of (subsets of) regular languages or learning of natural languages. We are interested in various restrictions on learning, such as consistency, conservativeness or set-drivenness, exemplifying various natural learning restrictions. Building on previous results from the literature, we provide several maps (depictions of all pairwise relations) of various groups of learning criteria, including a map for monotonicity restrictions and similar criteria and a map for restrictions on data presentation. Furthermore, we consider, for various learning criteria, whether learners can be assumed consistent.
