By Barbara Hammer, Andrej Gisbrecht, Alexander Schulz (auth.), Pablo A. Estévez, José C. Príncipe, Pablo Zegers (eds.)
Self-organizing maps (SOMs) have been constructed via Teuvo Kohonen within the early eighties. when you consider that then greater than 10,000 works were in line with SOMs. SOMs are unsupervised neural networks valuable for clustering and visualization reasons. Many SOM purposes were built in engineering and technological know-how, and different fields.
This ebook includes refereed papers offered on the 9th Workshop on Self-Organizing Maps (WSOM 2012) held on the Universidad de Chile, Santiago, Chile, on December 12-14, 2012. The workshop introduced jointly researchers and practitioners within the box of self-organizing structures. one of the ebook chapters there are very good examples of using SOMs in agriculture, desktop technological know-how, facts visualization, future health structures, economics, engineering, social sciences, textual content and photograph research, and time sequence research. different chapters current the most recent theoretical paintings on SOMs in addition to studying Vector Quantization (LVQ) equipment.
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Additional resources for Advances in Self-Organizing Maps: 9th International Workshop, WSOM 2012 Santiago, Chile, December 12-14, 2012 Proceedings
One example of the obtained results is shown in Fig. 8. The distance d is mentioned in the Fig. 8. 1, (e) 3 layers, d=5, and (f) 4 layers, d=5 Classification of Chain-Link and Other Data with Spherical SOM 41 caption of Fig. 8. In the case of 2 layers, Fig. 1. However, in the case of 3 layers and 4 layers, the boundaries could not be separated with a d equal to 1. Therefore, the cases of d=5, and 10 where the boundaries could be separated are shown respectively in (b), (e), and (c), (f). As shown in Fig.
Let's pay attention to the Cyan mark (red circled) on the U-matrix in the central left of the figure. The mark in Fig. 6(a) was moved about 10 steps to the right in Fig. 6(b), as the map is continuous in either side. Similarly, in Fig. 6(c), it is moved up by about 5 steps compared with Fig. 6(b). Fig. 6. (a) After Torus-SOM learning, (b) the map (a) is slightly moved to the right, and (c) moved upwards. (d) The result obtained with SOM_PAK learning. Finally, the result obtained after learning, using the SOM_PAK is shown in Fig.
This new classification method can be applied to various data sets. Here, we apply it to the chain-link problem  considered to be notoriously difficult, and to the problem of the separation of stacked random number planes . 1 The Comparison of the Results Obtained with the XOM Method and the Spherical SOM for a Benchmark Problem The benchmark problem consists of two rings (links) (Fig. 1(a)) with a distance d from each other and intersecting vertically in the three-dimensional space. A. Estévez et al.