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- W133604456 abstract "The training of Emergent Self-organizing Maps (ESOM )w ith large datasets can be a computationally demanding task. Batch learning may be used to speed up training. It is demonstrated here, however, that the representation of clusters in the data space on maps trained with batch learning is poor compared to sequential training. This effect occurs even for very clear cluster structures. The k-batch learning algorithm is preferrable, because it creates the same quality of representation as sequential learning but maintains important properties of batch learning that can be exploited for speedup." @default.
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- W133604456 date "2006-01-01" @default.
- W133604456 modified "2023-10-11" @default.
- W133604456 title "An algorithm for fast and reliable ESOM learning" @default.
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