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首页> 外文期刊>Neural Network World >PROCESSING AND CATEGORIZATION OF CZECH WRITTEN DOCUMENTS USING NEURAL NETWORKS
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PROCESSING AND CATEGORIZATION OF CZECH WRITTEN DOCUMENTS USING NEURAL NETWORKS

机译：使用神经网络对捷克书面文件进行处理和分类

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摘要

The Kohonen Self-organizing Feature Map (SOM) has been developed for clustering input vectors and for projection of continuous high-dimensional signal to discrete low-dimensional space. The application area, where the map can be also used, is the processing of text documents. Within the project WEBSOM, some methods based on SOM have been developed. These methods are suitable either for text documents information retrieval or for organization of large document collections. All methods have been tested on collections of English and Finnish written documents. This article deals with the application of WEBSOM methods to Czech written documents collections. The basic principles of WEBSOM methods, transformation of text information into the real components feature vector and results of documents classification are described. The Carpenter-Grossberg ART-2 neural network, usually used for adaptive vector clustering, was also tested as a document categorization tool. The results achieved by using this network are also presented.
机译：Kohonen自组织特征图（SOM）已开发用于聚类输入向量并将连续的高维信号投影到离散的低维空间。还可以使用地图的应用程序区域是文本文档的处理。在WEBSOM项目中，已经开发了一些基于SOM的方法。这些方法适用于文本文档信息检索或组织大型文档集合。所有方法都经过了英文和芬兰书面文件集的测试。本文介绍了WEBSOM方法在捷克书面文档集中的应用。描述了WEBSOM方法的基本原理，将文本信息转换为真实成分特征向量以及文档分类的结果。通常用于自适应矢量聚类的Carpenter-Grossberg ART-2神经网络也已作为文档分类工具进行了测试。还介绍了通过使用该网络获得的结果。

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