Show Reference: "Recursive self-organizing maps"

Recursive self-organizing maps Neural Networks, Vol. 15, No. 8-9. (v 2002), pp. 979-991 by Thomas Voegtlin
@article{voegtlin-2002,
    abstract = {This paper explores the combination of self-organizing map ({SOM}) and feedback, in order to represent sequences of inputs. In general, neural networks with time-delayed feedback represent time implicitly, by combining current inputs and past activities. It has been difficult to apply this approach to {SOM}, because feedback generates instability during learning. We demonstrate a solution to this problem, based on a nonlinearity. The result is a generalization of {SOM} that learns to represent sequences recursively. We demonstrate that the resulting representations are adapted to the temporal statistics of the input series.},
    author = {Voegtlin, Thomas},
    issn = {0893-6080},
    journal = {Neural Networks},
    keywords = {ann, learning, som, time-series, unsupervised-learning},
    number = {8-9},
    pages = {979--991},
    pmid = {12416688},
    posted-at = {2013-01-14 11:25:58},
    priority = {2},
    title = {Recursive self-organizing maps},
    url = {http://view.ncbi.nlm.nih.gov/pubmed/12416688},
    volume = {15},
    year = {2002}
}

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Recursive and Recurrent SOMs have been used for mapping temporal data.