A survey on instance selection for active learning
A survey on instance selection for active learning
复制标题
DOI：
10.1007/s10115-012-0507-8
复制
发表时间：
2013-05-01
影响因子：
2.7
通讯作者：
Li, Bin
中科院分区：
文献类型：
--
作者：
Fu, Yifan;Zhu, Xingquan;Li, Bin
主动学习的目标是通过标记信息量最大的实例，以最小的代价训练出一个准确的预测模型。在本文中，我们调查现有的工作，主动学习的实例选择的角度来看，并将它们分为两类的进步关系：（1）主动学习仅仅基于独立同分布（IID）的实例的不确定性，和（2）主动学习进一步考虑到实例的相关性。使用上述分类，我们总结了该领域的主要方法，沿着他们的技术优势/弱点，其次是一个简单的运行时性能比较，并讨论新兴的主动学习应用程序和实例选择的挑战。本调查旨在为主动学习提供一个高层次的总结，并激励感兴趣的读者考虑设计有效的主动学习解决方案的实例选择方法。
Active learning aims to train an accurate prediction model with minimum cost by labeling most informative instances. In this paper, we survey existing works on active learning from an instance-selection perspective and classify them into two categories with a progressive relationship: (1) active learning merely based on uncertainty of independent and identically distributed (IID) instances, and (2) active learning by further taking into account instance correlations. Using the above categorization, we summarize major approaches in the field, along with their technical strengths/weaknesses, followed by a simple runtime performance comparison, and discussion about emerging active learning applications and instance-selection challenges therein. This survey intends to provide a high-level summarization for active learning and motivates interested readers to consider instance-selection approaches for designing effective active learning solutions.