Adaptive Learning Algorithm in Tree-structured Self-organizing Feature Map
Adaptive Learning Algorithm in Tree-structured Self-organizing Feature Map
复制标题
树结构自组织特征图中的自适应学习算法
DOI：
10.14864/softscis.2010.0.1429.0
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
K. J. Mackin
中科院分区：
文献类型：
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作者：
Takashi Yamaguchi;T. Ichimura;K. J. Mackin
Map是一个分层的神经网络，由一个输入层和一个竞争层组成，用于数据可视化和矢量量化。SOM矢量量化的精度取决于竞争层神经元的数目。因此，当给定未知数据集时，很难确定足够的竞争层大小。在本文中，我们提出了一个分层竞争层适应方法，以找出足够数量的神经元。该方法利用相邻神经元之间的均值误差和使用频率来增加和删除神经元。
Map is a layered neural network consisting of an input layer and a competitive layer for the data visualization and vector quantization. The accuracy of SOM vector quantization depends on the number of competitive layer's neurons. Therefore, when an unknown data set is given, it is difficult to decide the sufficient competitive layer size. In this paper, we propose a hierarchical competitive layer adaptation method in order to find out the sufficient number of neurons. The proposed method adds and deletes neurons using the means error and frequency in use among neighboring neurons.