By Barbara Hammer, Andrej Gisbrecht, Alexander Schulz (auth.), Pablo A. Estévez, José C. Príncipe, Pablo Zegers (eds.)
Self-organizing maps (SOMs) have been built by way of Teuvo Kohonen within the early eighties. due to the fact then greater than 10,000 works were in line with SOMs. SOMs are unsupervised neural networks important for clustering and visualization reasons. Many SOM purposes were constructed in engineering and technological know-how, and different fields.
This booklet comprises refereed papers awarded 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 e-book chapters there are first-class examples of using SOMs in agriculture, laptop technological know-how, facts visualization, well-being platforms, economics, engineering, social sciences, textual content and snapshot 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
P. Engelbrecht part of many SOM-based EDA methods and all DM algorithms . The most common labeling methods are supervised [13,20], relying on classiﬁcation information in a labeling data set. However, since classiﬁcation information is often unavailable, or may bias the labeling if it is available, unsupervised labeling (which requires no data classiﬁcations) has been investigated [4,6,19,21]. This paper presents a novel unsupervised labeling method, called unsupervised weight-based cluster labeling.
N ) , which corresponds to the solution of the so-called dual eigen-problem (22) in , and, hence, the stability analysis can be taken from , which also delivers the extension to the full eigen-problem and the respective Sanger-algorithm. 2 Hebbian PCA Learning in (V, dB ) Here we consider the space (V, dB ) from (20) and exploit its isomorphism to the image space IγΦ ⊆ B of the kernel mapping Φ for a SIP-RKBS B. Again, we assume centralized kernels satisfying E [Ψ (v)] = 0. Further, we assume that the kernel γΦ takes only real values.
B) each of the coordinates (Fig. 2) on the B ring are on the spherical surface, and the arrow starts and ends in B(x,0,0). 38 M. Ohkita et al. Fig. 4. (a) the starting (blue) and (b) the end (black) position in Fig. 3 (a) (b) Fig. 5. Representation obtained when Fig. 4 is stretched to one dimension. In panels (a) and (b), the labels A and B, are the corresponding positions of start (left blue circled) and end (right black circled). 3 The Results Obtained with Other SOMs (Torus-SOM and SOM_PAK) Other SOM methods were also tried, namely, Torus-SOM  and SOM_PAK [5, 6].
Advances in Self-Organizing Maps: 9th International Workshop, WSOM 2012 Santiago, Chile, December 12-14, 2012 Proceedings by Barbara Hammer, Andrej Gisbrecht, Alexander Schulz (auth.), Pablo A. Estévez, José C. Príncipe, Pablo Zegers (eds.)