Self-organizing maps of massive document collections

被引：10
|
作者：
Kohonen, T [1 ]
机构：
[1] Aalto Univ, Neural Networks Res Ctr, FIN-02015 Espoo, Finland
来源：
IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL II | 2000年
关键词：
D O I：
10.1109/IJCNN.2000.857865
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Huge document collections can be organized according to textual similarities by the Self-Organizing Map (SOM) algorithm, when statistical representations of the textual contents are used as the feature vectors of the documents. In a practical experiment we mapped 6,840,568 patent abstracts onto a 1,002,240-node SOM. For the feature vectors we selected 500-dimensional random projections of the weighted word histograms.
引用
收藏
页码：3 / 9
页数：7
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