Information acquisition performance by supervised information-theoretic self-organizing maps
Information acquisition performance by supervised information-theoretic self-organizing maps
复制标题
DOI：
10.1109/nabic.2014.6921870
复制
发表时间：
2014-10
期刊：
影响因子：
--
通讯作者：
R. Kamimura
中科院分区：
文献类型：
--
作者：
R. Kamimura
在本文中，我们提出了一种新型的监督多层自组织映射，并检查在多大程度上可以增加多层网络中的信息内容。到目前为止，我们已经介绍了信息理论SOM在一个单一的层，以增加信息内容。然而，我们已经发现了一些情况下，信息内容不能增加单层网络。我们使用了多层网络，我们发现即使在更高的层中，互信息也倾向于增加。相应的U矩阵显示出更清晰的类结构，即使是更高的层。然后，我们将该方法应用于预测性能的改善。当层数选择适当时，可以提高预测性能。
In this paper, we propose a new type of supervised multi-layered self-organizing map and examine to what extent information content in multi-layered networks can be increased. We have so far introduced the information-theoretic SOM in a single layer for increasing information content. However, we have found some cases where information content cannot be increased by single-layer networks. We used the multi-layered network and we found that mutual information tended to increase even for higher layers. The corresponding U-matrices showed clearer class structure even for higher layers. Then, we applied the method to the improvement of prediction performance. The prediction performance could be improved when the number of layers was appropriately chosen.