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Recognition of Infrared Spectrum Data of Coal Mine Gas Based on Multiple Hyperplanes Classifier Method

机译:基于多超平面分类器方法的煤矿瓦斯红外光谱数据识别

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In light of the limitation of traditional linear searching way,which uses one hyperplane to classify in spectral recognition of coal mine gas,multiple hyperplanes of dendriform classifier method are introduced to classify in this paper,which has the good classifying effect on gas in complicated background environment. This article has introduced the principle of dendriform partition linear classifier,and used the dendriform classifier to do algorithm training and identificating classification. Applying to classify and identify complicated samples of remote sensing infrared spectrum datas of coal mine gas,the experimental results indicated that,in the same numbers of training sample,this method not only has less training iteration than linear classification,but also the weights calculated by it have better examination results than linear classification for the whole examination datas.
机译:鉴于传统线性搜索方式的限制,它使用一个超平面在煤矿气体的光谱识别中进行分类,引入了多档分类器方法的多个超平板,以对本文进行分类,这对复杂背景中的气体进行了良好的分类作用环境。本文介绍了树突分区线性分类器的原理,并使用了Dendriform分类器进行算法培训和识别分类。应用分类和识别煤矿气体的遥感红外光谱数据的复杂样本,实验结果表明,在相同数量的训练样本中,这种方法不仅具有比线性分类更少的训练迭代,而且还具有较低的培训迭代,还具有它具有比整个考试数据的线性分类更好的检查结果。

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