Appendix of Application of self-organizing maps to coal elemental data

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煤质元素分析

自组织映射聚类

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Appendix of Application of self-organizing maps to coal elemental data

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2023-08-22

Scaling Laws in City Growth: Setting Limitations with Self-Organizing Maps

城市标度律分析

自组织映射聚类

Do scaling relations always provide the means to anticipate the relationships between the size of cities, costs of maintenance, and the socio-economic benefits resulting from their growth? Scaling law

NIAID Data Ecosystem60

The intensity (stronger = darker shading) of nine density/biomass variables (A; used for SOM training) and eight explanatory variables (B; not used for SOM training) in particular SOM regions.

生物量监测

自组织映射聚类

Values of the A variables come from the virtual sampling terms (one per output neuron), whereas values of the B variables are the means of the real sampling terms assigned to a given output neuron (se

Figshare2016-02-24 更新40

Features of different SOM runs for the four classes of trisomic mice.

三体小鼠模型分析

自组织映射聚类

Bold indicates the best result. Features of different SOM runs for the four classes of trisomic mice.

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BARIA cohort: 106 participants clinical metadata summary for SOM-defined clusters.

临床患者分组

自组织映射聚类

BARIA cohort: 106 participants clinical metadata summary for SOM-defined clusters.

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Language intensity classification and Neuronal Networks

职业语言强度分类

自组织映射聚类

Kohonen Self-Organizing Maps (SOM) are a particular type of artificial neural network created by Teuvo Kohonen. Their unsupervised learning makes them suitable for application, among other things, to

NIAID Data Ecosystem40

Appendix of Application of self-organizing maps to coal elemental data

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