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.

中文翻译：

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用于学习可索引类的地图

我们研究从正数据中学习索引家庭，其中学习者可以自由选择一个假设空间（具有统一可判定的成员资格），至少包括要学习的语言。这抽象了一个非常普遍的学习任务，可以在许多领域找到，例如学习常规语言（的子集）或学习自然语言。我们对学习的各种限制感兴趣，例如一致性、保守性或集合驱动性，举例说明各种自然学习限制。基于先前的文献结果，我们提供了各种学习标准组的几个地图（所有成对关系的描述），包括单调性限制和类似标准的地图以及数据呈现限制的地图。此外，我们考虑，对于各种学习标准，