基于间隔惩罚的主动学习最近邻分类器
Nearest Neighbor Classifier with Margin Penalty for Active Learning
摘要：
随着深度学习成为自然语言处理领域的主流，对合适的主动学习方法的需求变得前所未有的迫切。提出了一种基于最近邻分类器的主动学习方法，并取得了较好的效果.然而，现有的最近邻分类器不适合分类互斥类，因为类间的差异不能保证最近邻分类器。结果，边缘区域中的信息样本不能被发现，并且AL性能受损。为此，我们提出了一种新的最近邻分类器与间隔惩罚主动学习（NCMAL）。首先，在类间加入强制性的间隔惩罚，从而保证了类间的差异性和类内的紧凑性。其次，提出了一种新的样本选择策略，以发现边缘区域内的信息样本。为了证明这些方法的有效性，我们使用其他最先进的方法对数据集进行了广泛的实验。实验结果表明，我们的方法取得了更好的结果与更少的注释样本比所有的基线方法。
英文摘要：
As deep learning becomes the mainstream in the field of natural language processing, the need for suitable active learning method are becoming unprecedented urgent. Active Learning (AL) methods based on nearest neighbor classifier are proposed and demonstrated superior results. However, existing nearest neighbor classifier are not suitable for classifying mutual exclusive classes because inter-class discrepancy cannot be assured by nearest neighbor classifiers. As a result, informative samples in the margin area can not be discovered and AL performance are damaged. To this end, we propose a novel Nearest neighbor Classifier with Margin penalty for Active Learning(NCMAL). Firstly, mandatory margin penalty are added between classes, therefore both inter-class discrepancy and intra-class compactness are both assured. Secondly, a novel sample selection strategy are proposed to discover informative samples within the margin area. To demonstrate the effectiveness of the methods, we conduct extensive experiments on for datasets with other state-of-the-art methods. The experimental results demonstrate that our method achieves better results with fewer annotated samples than all baseline methods.
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。
