版本空间搜索中主动学习方法的鉴别力与有效利用
On the Discrimination Power and Effective Utilization of Active Learning Measures in Version Space Search
摘要：
主动学习（AL）方法已被证明在许多应用领域比被动监督方法节省成本。一个主动的学习者，旨在找到一些目标假设，制定顺序查询一些神谕。与已经回答的查询一致的假设集称为版本空间。已经提出了几个查询选择措施（QSM），以确定最好的查询问下一个。假设binaryoutcome查询，我们分析各种QSM wrt。在当前版本空间内他们选择的查询的辨别力。因此，我们得出这些QSM之间的优越性和等价关系，并引入现有的QSM的改进版本，以克服确定的问题。所获得的图片给出了关于在基于池的AL场景中应该优选使用哪些QSM的提示。此外，我们推导出最优查询wrt的性质。QSM必须满足。在此基础上，我们展示了如何有效的启发式搜索方法的最佳查询，查询合成AL的情况下，可以设计。
英文摘要：
Active Learning (AL) methods have proven cost-saving against passive supervised methods in many application domains. An active learner, aiming to find some target hypothesis, formulates sequential queries to some oracle. The set of hypotheses consistent with the already answered queries is called version space. Several query selection measures (QSMs) for determining the best query to ask next have been proposed. Assuming binaryoutcome queries, we analyze various QSMs wrt. to the discrimination power of their selected queries within the current version space. As a result, we derive superiority and equivalence relations between these QSMs and introduce improved versions of existing QSMs to overcome identified issues. The obtained picture gives a hint about which QSMs should preferably be used in pool-based AL scenarios. Moreover, we deduce properties optimal queries wrt. QSMs must satisfy. Based on these, we demonstrate how efficient heuristic search methods for optimal queries in query synthesis AL scenarios can be devised.
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