Self-Organizing Maps, Third Edition

By Teuvo Kohonen. Springer Series in Information Sciences, 2001

1,044 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/929c0833ef4bdc53f59971c0486fd30412debc7d

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Citations in the corpus

Cost733cat - A database of weather and circulation type classifications (2010, neutral): “Also the radius R of the neighborhood around the winner unit is relatively large to start with, so as to include all neurons and is consecutively shrunk down to the point where only the winner unit is updated (Kohonen, 2001).”
Spectrum-Diverse Neuroevolution With Unified Neural Models (2017, methodology): “ VOL. X, NO. X, SEPTEMBER 20XX 8 Fig. 7. Evolution’s overview. with the input array if and only if the input array has higher novelty. This table shares some similarities with the self-organizing map [42] and the neural gas [43], but its behavior is independent of input frequency and it uses fewer c”
Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science (2016, methodology): “Here, in particular Kohonen Feature Maps (KFM) have been used to study tactical patterns (Barton et al. 2006; Bauer and Schöllhorn 1997; Dutt-Mazumder et al. 2011; Kohonen 1990, 2001; Lees and Barton 2003).”
Essentials of the self-organizing map (2013, methodology): “This concept was originally introduced by the author; cf. Kohonen (1985), but it can also be found from Kohonen (2001).”
Machine learning for large-scale crop yield forecasting (2020, methodology): “…deep neural networks to predict corn yield in the US; You et al. (2017) leveraged representation learning ideas to predict soybean yield in the US; and Pantazi et al. (2016) used self-organizing maps (Von der Malsburg 1973; Kohonen 2001) to predict within-field variation of wheat yield in the UK.”
BRINGING UP ROBOT: FUNDAMENTAL MECHANISMS FOR CREATING A SELF-MOTIVATED, SELF-ORGANIZING ARCHITECTURE (2005, methodology): “problems ranging from creating maps for classifying the World Wide Web to analyzing financial data (Kohonen, 2001).”
Comprehensive survey on hierarchical clustering algorithms and the recent developments (2022, methodology): “SOM can supply the data visualization and an elegant topology diagram.”