An intelligent data analysis approach using self-organising-maps

C. Fung, Kok Wai Wong, D. Myers
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引用次数: 1

Abstract

A neural network-based data analysis model for the prediction and classification of field data has many attractions. However, there are problems in ensuring the generalisation capability of the data analysis model, in measuring the similarity between the original training data and the new unknown data and in processing large data volumes. This paper reports the use of self-organising maps (SOM) to overcome these difficulties and illustrates the utilisation of this approach though applications in the agricultural, resource exploration and mineral processing areas.
使用自组织地图的智能数据分析方法
基于神经网络的数据分析模型对野外数据进行预测和分类具有许多优点。然而,在保证数据分析模型的泛化能力、测量原始训练数据与新的未知数据之间的相似度以及处理大数据量方面存在问题。本文报道了使用自组织地图(SOM)来克服这些困难,并举例说明了这种方法在农业、资源勘探和矿物加工领域的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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