Statistical Asymptotic Theory of Active Learning
Statistical Asymptotic Theory of Active Learning
复制标题
主动学习的统计渐近理论
DOI：
10.1023/a:1022446624428
复制
发表时间：
2002
影响因子：
1
通讯作者：
T. Kanamori
中科院分区：
文献类型：
--
作者：
T. Kanamori
我们研究了一个参数估计问题。我们的目标是估计或识别条件概率，即系统。我们假设当我们收集训练数据时，我们可以为系统选择合适的输入。这种估计在人工神经网络中被称为主动学习。本文提出了一种新的主动学习算法，并利用统计渐近理论对算法的风险进行了评价。这些算法被看作是两阶段抽样实验设计的一个版本。通过简单的计算机仿真验证了主动学习的有效性。
We study a parametric estimation problem. Our aim is to estimate or to identify the conditional probability which is called the system. We suppose that we can select appropriate inputs to the system when we gather the training data. This kind of estimation is calledactive learningin the context of the artificial neural networks. In this paper we suggest new active learning algorithms and evaluate the risk of the algorithms by using statistical asymptotic theory. The algorithms are regarded as a version of the experimental design with two-stage sampling. We verify the efficiency of the active learning by simple computer simulations.