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如何为 Kohonen 的 SOM 选择合适的网格数?

[英]How to choose appropriate number of grid for Kohonen's SOM?

I wonder how to choose the number of grids in Kohonen SOM.我想知道如何在 Kohonen SOM 中选择网格数。 Also, what will happen when the number of grids increases?另外,当网格数量增加时会发生什么?

In general, when you increase the number of model parameters, you increase the ability of the model to adapt to more complex problems.一般来说,当您增加模型参数的数量时,您会增加模型适应更复杂问题的能力。 For SOMs, this is the case as well.对于 SOM,情况也是如此。 But, the neurons are still connected in a neighborhood relation - so the effect is not linear.但是,神经元仍然以邻域关系连接 - 因此效果不是线性的。

As a practical guide, you can have a look at the documentation of the Python package susi , where the grid size of the SOM, as well as other hyperparameters, are discussed (with literature references): https://susi.readthedocs.io/en/latest/hyperparameters.html作为实用指南,您可以查看 Python 包susi的文档,其中讨论了 SOM 的网格大小以及其他超参数(附有文献参考): https : //susi.readthedocs.io /en/latest/hyperparameters.html

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