Incremental projection learning for optimal generalization
Incremental projection learning for optimal generalization
复制标题
用于最佳泛化的增量投影学习
DOI：
10.1016/s0893-6080(00)00080-0
复制
发表时间：
2001
期刊：
影响因子：
--
通讯作者：
H. Ogawa
中科院分区：
文献类型：
--
作者：
Masashi Sugiyama;H. Ogawa
在神经网络学习的许多实际情况中，通常期望在学习过程已经完成之后进一步提高泛化能力。一种常见的方法是将训练数据添加到神经网络中。鉴于人类的学习方法，在先前结果的基础上建立后验学习结果似乎是很自然的，这通常被称为增量学习。到目前为止，已经设计了许多增量学习方法。然而，与批处理学习方法相比，它们提供了较差的泛化能力。本文提出了一种在噪声环境下的增量投影学习方法，该方法可以获得与批量投影学习完全相同的学习结果。通过计算机仿真验证了该方法的有效性。
In many practical situations in neural network learning, it is often expected to further improve the generalization capability after the learning process has been completed. One of the common approaches is to add training data to the neural network. In view of the learning methods of human beings, it seems natural to build posterior learning results upon prior results, which is generally referred to as incremental learning. Many incremental learning methods have been devised so far. However, they provide poor generalization capability compared with batch learning methods. In this paper, a method of incremental projection learning in the presence of noise is presented, which provides exactly the same learning result as that obtained by batch projection learning. The effectiveness of the presented method is demonstrated through computer simulations.