EGAL: Exploration Guided Active Learning for TCBR
EGAL: Exploration Guided Active Learning for TCBR
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EGAL：TCBR 探索引导主动学习
DOI：
10.1007/978-3-642-14274-1_13
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发表时间：
2010
影响因子：
5.3
通讯作者：
Brian Mac Namee
中科院分区：
文献类型：
--
作者：
Rong Hu;Sarah Jane Delany;Brian Mac Namee
建立标记的案例库的任务可以使用主动学习（AL）来实现，这是一个便于以最小的手动标记工作标记大量实例的过程。设计人工智能系统的主要挑战是选择策略的发展，选择最翔实的例子手动标记。典型的选择策略使用开发技术，试图根据分类器的输出来细化决策空间的不确定区域。其他方法倾向于平衡开发和探索，从领域空间的密集和有趣的区域中选择示例。在本文中，我们提出了一个简单而有效的探索，只有选择策略的AL在文本域。我们的方法本质上是基于案例的，只使用基于最近邻的密度和多样性措施。我们展示了它的性能如何与计算成本更高的基于开发的方法相媲美，并且它提供了独立于分类器的机会。
The task of building labelled case bases can be approached using active learning (AL), a process which facilitates the labelling of large collections of examples with minimal manual labelling effort. The main challenge in designing AL systems is the development of a selection strategy to choose the most informative examples to manually label. Typical selection strategies use exploitation techniques which attempt to refine uncertain areas of the decision space based on the output of a classifier. Other approaches tend to balance exploitation with exploration, selecting examples from dense and interesting regions of the domain space. In this paper we present a simple but effective exploration-only selection strategy for AL in the textual domain. Our approach is inherently case-based, using only nearest-neighbour-based density and diversity measures. We show how its performance is comparable to the more computationally expensive exploitation-based approaches and that it offers the opportunity to be classifier independent.
基于案例的推理研究与开发
DOI：
10.1007/978-3-642-39056-2_11
发表时间：
2013
期刊：
--
影响因子：
--
作者：
Horsburgh B
通讯作者：
Horsburgh B