By Teuvo Kohonen (auth.), Dr. Udo Seiffert, Professor Lakhmi C. Jain (eds.)

The Self-Organizing Map (SOM) is without doubt one of the most often used architectures for unsupervised man made neural networks. brought by way of Teuvo Kohonen within the Nineteen Eighties, SOMs were built as the most important approach for visualisation and unsupervised class initiatives through an energetic and cutting edge neighborhood of interna­ tional researchers. a couple of extensions and variations were constructed over the last twenty years. the reason being absolutely no longer that the unique set of rules was once imperfect or inad­ equate. it is vitally the common applicability and simple dealing with of the SOM. Com­ pared to many different community paradigms, just a couple of parameters have to be prepared and therefore additionally for a newbie the community ends up in precious and trustworthy effects. by no means­ theless there's scope for advancements and complex new advancements as this ebook impressively demonstrates. The variety of released purposes using the SOM seems to be never-ending. because the identify of this publication shows, the reader will make the most of a number of the most modern the­ oretical advancements and should turn into familiar with a couple of difficult real-world purposes. Our objective in generating this ebook has been to supply an up­ to-date therapy of the sector of self-organizing neural networks, that allows you to be ac­ cessible to researchers, practitioners and graduated scholars from assorted disciplines in teachers and undefined. we're very thankful to the daddy of the SOMs, Professor Teuvo Kohonen for sup­ porting this publication and contributing the 1st chapter.

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Ir), further := wi - wi- and := Wi+ - Wi. 5) 26 Daniel Polani v ... ~= ... k-th grid direction Fig. 5. Local deformation at a neuron where ¢k is the angle L(vi;, vt) between v; and vt. The geometric situation can be visualized as in Fig. 5. The average of c( i) on all inner neurons of A will be called /-LATA· As /-LATA assumes a rectangular grid as topology of A, it presupposes significantly more structure than other organization measures only assuming some graph structure on A and one could hope to relate this measure to some of the well known curvature measures of the continuous case.

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Self-Organizing Neural Networks: Recent Advances and by Teuvo Kohonen (auth.), Dr. Udo Seiffert, Professor Lakhmi C.
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