Wu J, Yan H, Chalmers A
Department of Electrical Engineering, University of Sydney, NSW, Australia.
Int J Neural Syst. 1994 Dec;5(4):357-62. doi: 10.1142/s0129065794000347.
In this paper, we present a two-layer self-organizing neural network based method for handwritten digit recognition. The network consists of a base layer self-organizing map and a set of corresponding maps in the second layer. The input patterns are partitioned into subspace in the first layer. Patterns in a subspace are led to the second layer and a corresponding map is built according to the first layer performance. In the classification process, each pattern searches for several closest nodes from the base map and then it is classified into a specified class by determining the nearest model of the corresponding maps in the second layer. The new method yielded higher accuracy and faster performance than the ordinary self-organizing neural network.
在本文中，我们提出了一种基于两层自组织神经网络的手写数字识别方法。该网络由一个基础层自组织映射和第二层中的一组相应映射组成。输入模式在第一层被划分为子空间。子空间中的模式被引导到第二层，并根据第一层的性能构建相应的映射。在分类过程中，每个模式从基础映射中搜索几个最接近的节点，然后通过确定第二层中相应映射的最近模型将其分类到指定类别。与普通自组织神经网络相比，新方法具有更高的准确率和更快的性能。
