Quantile Search with Time-Varying Search Parameter
Quantile Search with Time-Varying Search Parameter
复制标题
使用时变搜索参数的分位数搜索
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Gautam Dasarathy
中科院分区：
文献类型：
--
作者：
J. Lipor;Gautam Dasarathy
我们在空间采样的背景下考虑主动学习问题，其中采样成本是采样过程中采样数量和移动距离的函数。我们提出了统一到二进制（UTB）搜索，这是一种新的算法。UTB搜索扩展了分位数搜索（QS）算法[1]，从而允许在整个搜索过程中改变调优参数m。我们从样本复杂度和行进距离两方面分析了该算法。实证结果表明，在所有考虑的情况下，我们提出的方法都优于固定m的QS。
We consider the problem of active learning in the context of spatial sampling, where the sampling cost is a function of both the number of samples taken and the distance traveled during the sampling procedure. We present Uniform-to-Binary (UTB) search, a novel algorithm in this setting. UTB search extends the Quantile Search (QS) algorithm [1] such that the tuning parameter m is allowed to vary throughout the search procedure. We analyze the algorithm in terms of both sample complexity and distance traveled. Empirical results show that our proposed method outperforms QS with fixed m in all cases considered.