我们需要多少种不同/核自组织映射变体？
How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?
摘要：
在许多应用环境中，数据过于丰富和复杂，无法用数字向量表示。将机器学习和数据挖掘技术扩展到此类数据的一般方法是真正基于相异性或基于测量两个对象的不同或相似程度的内核。这种方法已被用来定义自组织映射（SOM）的几个变种。本文回顾了这些变体使用一套共同的符号，以概述它们之间的差异和相似之处。它讨论的优点和缺点的变种，以及实际相关的相异性/内核SOM的实际应用。
英文摘要：
In numerous applicative contexts, data are too rich and too complex to be represented by numerical vectors. A general approach to extend machine learning and data mining techniques to such data is to really on a dissimilarity or on a kernel that measures how different or similar two objects are. This approach has been used to define several variants of the Self Organizing Map (SOM). This paper reviews those variants in using a common set of notations in order to outline differences and similarities between them. It discusses the advantages and drawbacks of the variants, as well as the actual relevance of the dissimilarity/kernel SOM for practical applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
