Generalization of topology preserving maps: A graph approach

被引：0
|
作者：
Barsi, A [1 ]
机构：
[1] Budapest Univ Technol & Econ, Dept Photogrammetry & Geoinformat, H-1111 Budapest, Hungary
来源：
2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS | 2004年
关键词：
D O I：
10.1109/IJCNN.2004.1380025
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The paper presents a novel algorithm, which is based on the self-organizing map (SOM) method. The combination of an undirected acyclic graph with the Kohonen learning rule results the efficient Self-Organizing Neuron Graph (SONG) algorithm. It has two modi: one is based on the adjacency information of the neuron graph, the other integrates an all-pair shortest path function, which permanently updates a generalized distance matrix. The newly developed SONG techniques were involved in pattern recognition tasks, where they proved their efficiency and flexibility.
引用
收藏
页码：809 / 813
页数：5
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