学习可索引类的映射
Maps for Learning Indexable Classes
摘要：
我们研究学习的索引家庭从积极的数据，学习者可以自由地选择一个假设空间（具有一致可判定的成员），包括至少要学习的语言。这抽象了一个非常普遍的学习任务，可以在许多领域找到，例如正则语言（子集）的学习或自然语言的学习。我们对学习的各种限制感兴趣，例如一致性，保守性或集合驱动性，举例说明各种自然的学习限制。建立在以前的结果，从文献中，我们提供了几个地图（所有成对关系的解释）的各种学习标准，包括单调性限制和类似的标准和地图的限制数据呈现。此外，我们考虑，对于各种学习标准，是否可以假设学习者一致。
英文摘要：
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.
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。
