论文标题
通过成本感知学习的模型探索
Model Exploration with Cost-Aware Learning
论文作者
论文摘要
我们提出了明确考虑非恒定成本的主动学习程序的扩展。这项工作考虑了已知和未知的成本，并为学习者介绍了ε-柔韧的一词，这些学习者不仅考虑将总成本降至最低，而且还可以探索样本空间的高成本区域。我们在一个著名的机器学习数据集上演示了我们的扩展，发现ε-柔软的学习者的表现优于以已知成本和随机抽样的学习者。
We present an extension to active learning routines in which non-constant costs are explicitly considered. This work considers both known and unknown costs and introduces the term ε-frugal for learners that do not only consider minimizing total costs but are also able to explore high cost regions of the sample space. We demonstrate our extension on a well-known machine learning dataset and find that out ε-frugal learners outperform both learners with known costs and random sampling.
