(批量)SOM(自组织图，又名"Kohonen地图")的收敛标准? [英] Convergence criterion for (batch) SOM (Self-Organizing Map, aka "Kohonen Map")?
问题描述
我希望在Batch SOM收敛时停止执行. 我可以使用什么误差函数来确定收敛性?
I like to stop the execution when Batch SOM becomes converged. What error function can I use to determine the convergence?
推荐答案
在谈论SOM的收敛时，对于给定的映射大小(nxm)，您想知道算法是否已经进行了足够的迭代以确保映射是稳定的".宽松地说，这意味着如果对地图进行多次重新训练，是否会将地图的新输入(观察)置于相同的神经元/密码本向量上?每次都接受培训，只要集群仍以稳定的方式排列，就可以了.
When talking about convergence for SOMs, for a given map size (n x m), you want to know whether sufficient iterations of the algorithm have run to ensure the map is "Stable". This means, loosely speaking, do new inputs (observations) to the map get placed at the same neurons /codebook vectors if the map is retrained many times?(Ignoring the issue of the fact that the arrangement of the map may switch around when it is trained each time, which is fine as long as the clusters are still arranged in a stable way).
为帮助回答是否已运行足够的迭代问题，请参见下面列出的学术论文.这两篇论文都谈到了合适的地图尺寸(n x m值有助于确保SOM收敛吗?)的问题.
To assist in answering the question of whether enough iterations have run, see the academic papers listed below. Both papers also touch on the issue of what map size is appropriate (what n x m values help ensure convergence of the SOM?).
以下是论文中流行的一种传统方法:
One of the traditional approaches that has been popular in papers is given here:
评估自组织地图可靠性的统计工具(Bodt，Cottrell，Verleysen)
最近，这种方法问世了，看起来很有希望:
More recently, this method has come about, which looks rather promising:
针对自组织地图的收敛标准 ，硕士论文，本杰明h. ott(罗德岛大学)
我认为，这篇论文的确写得很好，并且阅读起来很愉快.很好的是，这项研究已作为SOM收敛测试写在R中的一个(很少为人所知)的程序包中，称为popsom.签出:
This thesis, in my opinion, was really well written and a pleasure to read. What's also nice is that this research has been written up as a SOM convergence test in a (rather unknown) package in R, called popsom. Check it out:
这篇关于(批量)SOM(自组织图，又名"Kohonen地图")的收敛标准?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
