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The modular network self-organizing map (mn-SOM) is a generalization of SOMs, where each node represents a parametric function such as a multi-layer perceptron or another SOM. Since given datasets are fewer than the nodes in general, some nodes never win in competition and have to update their parameters from the winners in the neighborhood, which can be regarded as interpolation. This study derived...
This paper reports on novel developments brought by combining the self-organizing map (SOM) with a modular network. This is called modular network SOM (mnSOM). The mnSOM has an arrayed structure of functional modules aligned on a lattice, and the architecture looks similar to the column structure of our cortex. One of the advantages of the mnSOM is that the user can design the module architecture...
This paper presents a new development of self-organizing maps (SOM), realized by combining them with the idea of a modular network. This we called a modular network SOM (mnSOM) in which each reference vector unit of a conventional SOM is replaced by a functional module. Since users can choose the functional module from any trainable architecture such as neural networks, the mnSOM is very flexible...
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