Incremental Active Learning for Optimal Generalization
Incremental Active Learning for Optimal Generalization
复制标题
增量主动学习以实现最佳泛化
DOI：
10.1162/089976600300014773
复制
发表时间：
2000
影响因子：
2.9
通讯作者：
H. Ogawa
中科院分区：
文献类型：
--
作者：
Masashi Sugiyama;H. Ogawa
设计用于最佳概括的输入信号的问题称为主动学习。在本文中，我们提供了一个两阶段的采样方案，以减少偏差和方差，并基于此方案，我们提出了两种主动学习方法。一种是适用于任意模型的多点搜索方法。通过计算机模拟显示了该方法的有效性。另一种是三角多项式模型中的最佳抽样方法。此方法精确地指定了最佳采样位置。
The problem of designing input signals for optimal generalization is called active learning. In this article, we give a two-stage sampling scheme for reducing both the bias and variance, and based on this scheme, we propose two active learning methods. One is the multipoint search method applicable to arbitrary models. The effectiveness of this method is shown through computer simulations. The other is the optimal sampling method in trigonometric polynomial models. This method precisely specifies the optimal sampling locations.