Kamimura Ryotaro
Information Science Laboratory, Tokai University, 1117 Kitakaname Hiratsuka Kanagawa 259-1292, Japan.
Int J Neural Syst. 2004 Feb;14(1):9-26. doi: 10.1142/S012906570400184X.
In this paper, we extend our greedy network-growing algorithm to multi-layered networks. With multi-layered networks, we can solve many complex problems that single-layered networks fail to solve. In addition, the network-growing algorithm is used in conjunction with teacher-directed learning that produces appropriate outputs without computing errors between targets and outputs. Thus, the present algorithm is a very efficient network-growing algorithm. The new algorithm was applied to three problems: the famous vertical-horizontal lines detection problem, a medical data problem and a road classification problem. In all these cases, experimental results confirmed that the method could solve problems that single-layered networks failed to. In addition, information maximization makes it possible to extract salient features in input patterns.
在本文中，我们将贪婪网络生长算法扩展到多层网络。借助多层网络，我们可以解决许多单层网络无法解决的复杂问题。此外，网络生长算法与教师指导学习相结合使用，这种学习方式无需计算目标与输出之间的误差就能产生合适的输出。因此，当前算法是一种非常高效的网络生长算法。新算法被应用于三个问题：著名的垂直线-水平线检测问题、一个医学数据问题和一个道路分类问题。在所有这些情况下，实验结果证实该方法能够解决单层网络未能解决的问题。此外，信息最大化使得提取输入模式中的显著特征成为可能。
