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Abstract: A temporal extension of the Self-Organizing Map (SOM) is presented. The learning algorithm is based on self-reference, and is called Recursive SOM. This network learns local representations of the temporal context associated with a time series, and extends classical properties of SOM to time. (Update)
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BibTeX entry: (Update)
T. Voegtlin. Recursive self-organizing maps. Neural Networks, 15(8-9):979991, 2002. http://citeseer.ist.psu.edu/voegtlin02recursive.html More
@misc{ voegtlin02recursive,
author = "T. Voegtlin",
title = "Recursive self-organizing maps",
text = "T. Voegtlin. Recursive self-organizing maps. Neural Networks, 15(8-9):979991,
2002.",
year = "2002",
url = "citeseer.ist.psu.edu/voegtlin02recursive.html" }
Citations (may not include all citations):1213 Self-Organizing Maps (context) - Kohonen - 1997
644 Finding structure in time - Elman - 1990
127 Oscillations and chaos in physiological control systems (context) - Mackey - 1977
40 The temporal Kohonen map (context) - Chappell, Taylor - 1993
19 the Analysis of Pattern Sequences by Self-Organizing Maps - Kangas - 1994
19 SARDNET: a self-organizing feature map for sequences - James, Miikkulainen - 1995
17 Using the som and local models in time-series prediction - Vesanto - 1997
9 Time series prediction using recurrent SOM with local linear.. - Koskela, Varsta et al. - 1998
7 Unsupervised learning of sequences on maps with lateral conn.. (context) - Kopecz - 1995
7 Context quantization and contextual self-organizing maps - Voegtlin - 2000
4 An extended Kohonen feature map for sentence recognition - Hoekstra, Drossaers - 1993
2 Spatio-temporal self-organizing feature maps - Euliano, Principe - 1996
The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://www.ini.unizh.ch/~thomas/):
Learning High-Degree Sequences in a Linear Network - Voegtlin, Dominey (2001) (Correct)
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