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- W2064272063 abstract "The self-organizing feature map (SOM) is an unsupervised neural network. It preserves a high-dimensional training data space's approximate characteristics, while scaling it to a two-dimensional grid. Few SOM-based rule extraction methods exist, and little analysis has been done on their overall viability. This paper presents the novel HybridSOMframework, which allows the combination of a SOM with any standard rule extraction algorithm, creating a customized hybrid rule extractor. Some HybridSOMvariations and traditional rule extraction algorithms are empirically compared, and the framework is critically discussed. This analysis also points to new conclusions on the viability of SOM-based rule extraction, in general." @default.
- W2064272063 created "2016-06-24" @default.
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- W2064272063 date "2009-03-01" @default.
- W2064272063 modified "2023-09-26" @default.
- W2064272063 title "HybridSOM: A generic rule extraction framework for self-organizing feature maps" @default.
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- W2064272063 doi "https://doi.org/10.1109/cidm.2009.4938624" @default.
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