Self-Organizing Neural Networks: Recent Advances and ApplicationsUdo Seiffert Springer Science & Business Media, 2001. gada 25. sept. - 278 lappuses The Self-Organizing Map (SOM) is one of the most frequently used architectures for unsupervised artificial neural networks. Introduced by Teuvo Kohonen in the 1980s, SOMs have been developed as a very powerful method for visualization and unsupervised classification tasks by an active and innovative community of interna tional researchers. A number of extensions and modifications have been developed during the last two decades. The reason is surely not that the original algorithm was imperfect or inad equate. It is rather the universal applicability and easy handling of the SOM. Com pared to many other network paradigms, only a few parameters need to be arranged and thus also for a beginner the network leads to useful and reliable results. Never theless there is scope for improvements and sophisticated new developments as this book impressively demonstrates. The number of published applications utilizing the SOM appears to be unending. As the title of this book indicates, the reader will benefit from some of the latest the oretical developments and will become acquainted with a number of challenging real-world applications. Our aim in producing this book has been to provide an up to-date treatment of the field of self-organizing neural networks, which will be ac cessible to researchers, practitioners and graduated students from diverse disciplines in academics and industry. We are very grateful to the father of the SOMs, Professor Teuvo Kohonen for sup porting this book and contributing the first chapter. |
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Citi izdevumi - Skatīt visu
Self-Organizing Neural Networks: Recent Advances and Applications Udo Seiffert Ierobežota priekšskatīšana - 2013 |
Self-Organizing Neural Networks: Recent Advances and Applications Udo Seiffert Priekšskatījums nav pieejams - 2014 |
Bieži izmantoti vārdi un frāzes
2-Phase-RBF adaptation algorithm analysis applications Artificial Neural Networks auditory basis function biological classification cluster centers cochlear Correlation Dimension data points data set defined Delaunay density model digits dimension dimensional distance distribution error function Feature Maps firing rate graph grid hyperspectral hyperspectral images IEEE implementations input data input neuron input patterns input space input vector k-means kernel Kohonen lateral inhibition learning rule Lyapunov Exponents matrix methods metric networks of spiking Neural Computation nodes optimal organization measure output space parameters phase learning Proc procedure processing units prototypes QFDN radial basis function random RBF centers RBF network RBF neuron receptive fields recognition RHO RHO RHO sample segment Self-Organizing Maps sequence signal SOMS spectral speech spiking neurons structure submaps subset synapses temporal temporal coding tion topographic topology preserving two-dimensional unsupervised learning values variables vector quantization Villmann visual Voronoi cell winner
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