论文标题
通过反距离加权进行积极学习进行回归
Active Learning for Regression by Inverse Distance Weighting
论文作者
论文摘要
本文提出了一种主动学习（AL）算法来解决基于反距离加权函数的回归问题，以选择要查询的特征向量。该算法具有以下功能：（i）支持基于池和基于人群的抽样； （ii）不是针对特定类别的预测因子量身定制的； （iii）可以在可查询特征向量上处理已知和未知的约束； （iv）可以顺序或批处理模式运行，具体取决于预测变量的频率。该方法的电势显示在有关说明性合成问题和现实世界数据集的数值测试中。该算法的实现，我们称为理想（基于逆距离的主动学习探索），可在http://cse.lab.imtlucca.it/~bemporad/ideal上获得。
This paper proposes an active learning (AL) algorithm to solve regression problems based on inverse-distance weighting functions for selecting the feature vectors to query. The algorithm has the following features: (i) supports both pool-based and population-based sampling; (ii) is not tailored to a particular class of predictors; (iii) can handle known and unknown constraints on the queryable feature vectors; and (iv) can run either sequentially, or in batch mode, depending on how often the predictor is retrained. The potentials of the method are shown in numerical tests on illustrative synthetic problems and real-world datasets. An implementation of the algorithm, which we call IDEAL (Inverse-Distance based Exploration for Active Learning), is available at http://cse.lab.imtlucca.it/~bemporad/ideal.
