By Thomas Villmann, Frank-Michael Schleif, Marika Kaden, Mandy Lange (eds.)
The booklet collects the medical contributions offered on the tenth Workshop on Self-Organizing Maps (WSOM 2014) held on the collage of technologies Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. beginning with the 1st WSOM-workshop 1997 in Helsinki this workshop makes a speciality of most up-to-date ends up in the sphere of supervised and unsupervised vector quantization like self-organizing maps for facts mining and information classification.
This tenth WSOM introduced jointly greater than 50 researchers, specialists and practitioners within the appealing small city Mittweida in Saxony (Germany) within sight the mountains Erzgebirge to debate new advancements within the box of unsupervised self-organizing vector quantization platforms and studying vector quantization methods for category. The ebook includes the approved papers of the workshop after a cautious evaluate strategy in addition to summaries of the invited talks. between those e-book chapters there are very good examples of using self-organizing maps in agriculture, desktop technology, info visualization, well-being structures, economics, engineering, social sciences, textual content and snapshot research and time sequence research. different chapters current the newest theoretical paintings on self-organizing maps in addition to studying vector quantization tools, reminiscent of referring to these easy methods to classical statistical selection methods.
All the contribution exhibit that vector quantization tools hide a wide range of software parts together with facts visualization of high-dimensional complicated facts, complex selection making and type or info clustering and knowledge compression.
Read Online or Download Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014 PDF
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Extra info for Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014
2 A Self-organizing Dynamic Neural Field We now embed the neural ﬁeld equation within a learning architecture similar to Kohonen Self-Organizing Maps. Indeed, we consider the dynamical system given by Eq. (4). N ] in the neural ﬁeld. At regular time intervals, a sample ζ, drawn from an unknown distribution D ⊆ R2 , is presented to the ﬁeld. The input I(x, t) at each position is computed as a Gaussian, with standard deviation σ, of ζ centered on p(x, t). Importantly, the prototypes are updated at every iteration and not just when the ﬁeld has converged.
Distance to ordinate axis : M F2 (i, X ) = abs(wi2 ). Distance to point (0,0): M F3 (i, X ) = wi . Distance to the mean of dataset: M F4 (i, X ) = wi − x mean . Figure 1 shows some results for SOM and MS-SOM (with 80 units) of the grid representation over the data space. Figure 1(a) shows the corresponding D-matrix (mean distances from weights of neighbour units), and the magnitude map for one MS-SOM. Figure 1(b) shows the typical result of a trained SOM where units tend to allocate their centroids in areas with higher data density.
Karlsruhe (1994) 9. fr Abstract. In a number of real-life applications, the user is interested in analyzing non vectorial data, for which kernels are useful tools that embed data into an (implicit) Euclidean space. However, when using such approaches with prototype-based methods, the computational time is related to the number of observations (because the prototypes are expressed as convex combinations of the original data). Also, a side eﬀect of the method is that the interpretability of the prototypes is lost.