By Thomas Villmann, Frank-Michael Schleif, Marika Kaden, Mandy Lange (eds.)
The ebook collects the medical contributions provided on the tenth Workshop on Self-Organizing Maps (WSOM 2014) held on the college 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 modern leads to the sector of supervised and unsupervised vector quantization like self-organizing maps for facts mining and knowledge classification.
This tenth WSOM introduced jointly greater than 50 researchers, specialists and practitioners within the attractive 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 ways for category. The publication comprises the approved papers of the workshop after a cautious overview procedure in addition to summaries of the invited talks. between those publication chapters there are first-class examples of using self-organizing maps in agriculture, computing device technological know-how, information visualization, wellbeing and fitness platforms, economics, engineering, social sciences, textual content and photo research and time sequence research. different chapters current the newest theoretical paintings on self-organizing maps in addition to studying vector quantization tools, comparable to concerning these the way to classical statistical selection methods.
All the contribution show that vector quantization equipment hide a wide variety of program parts together with information visualization of high-dimensional advanced info, complex choice making and class or information clustering and knowledge compression.
Read 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 resources for Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014
High accumulated magnitude means high learning up to the moment, and therefore unit becomes practically static. 4. The value of β, the forgetting factor. Using the deﬁnition of learning factor of (9), when β is equal to one, unit weights become the running weighted mean of the value of the data samples belonging to its Voronoi region, and adjacent regions (weighted according to its neighbourhood). To the contrary, lower values of β means that recent patterns have higher importance in the running weighted mean.
Then the same process would apply and, step by step, all the prototypes would converge to the center of mass of the distribution. In the simulation, we observe that the prototypes correctly self-organize. As a second example, we now consider a continuous stream of inputs. The point of this simulation is to demonstrate the ability of the neural ﬁeld to selforganize from a continuous stream of inputs without requiring an explicit reset signal. Indeed, we present to the network a small patch of 100 × 100 pixels on which a rotating bar is drawn.
Then, introducing adaptive prototypes for deﬁning the input, we show experimentally that such a neural ﬁeld can actually self-organize in a way similar to Kohonen’s self-organizing maps . 1 Results Detecting Where the Input Is Locally Maximal When self-organizing maps were introduced by , the author identiﬁed several processes “1. Formation of an activity cluster in the array around the unit at which activation is maximum. 2. Adaptive change in the input weights of those units where activity was conﬁned ”.