Titlebook: Self-Organizing Neural Networks; Recent Advances and Udo Seiffert,Lakhmi C. Jain Book 2002 Springer-Verlag Berlin Heidelberg 2002 Extensio

SYN

征税

1434-9922 of current SOM researchThe 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 a

极小量

聋子

Extensions and Modifications of the Kohonen-SOM and Applications in Remote Sensing Image Analysis,veral recent extensions to the original Kohonen SOM are discussed, emphasizing the necessity of faithful topological mapping for correct interpretation. The effectiveness of the presented approaches is demonstrated through case studies on real-life multi- and hyperspectral images.

PANEL

Book 2002n 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 sur

法律的瑕疵

鞭子

Unsupervised Learning and Self-Organization in Networks of Spiking Neurons,on potentials or spikes. In this chapter we investigate possible mechanisms of unsupervised learning and self-organization in networks of spiking neurons. After giving a brief introduction to spiking neuron networks we describe a biologically plausible algorithm for these networks to find clusters i

Chameleon

Generative Probability Density Model in the Self-Organizing Map,ulty to treat the method as a statistical model fitting procedure. In this chapter we give a short review of statistical approaches for the SOM. Then we present the probability density model for which the SOM training gives the maximum likelihood estimate. The density model can be used to choose the

信徒

Growing Multi-Dimensional Self-Organizing Maps for Motion Detection,o reduce the dimensionality of the input data. However, occasionally a multi-dimensional layer, keeping more than two dimensions of the input data, might be more advantageous. This sometimes also called hypercube topology can be considered as the universal case of the standard topology. This chapter

Hormones

Extensions and Modifications of the Kohonen-SOM and Applications in Remote Sensing Image Analysis,he past ten years. Hyperspectral sensors, in particular, are capable of capturing the detailed spectral signatures that uniquely characterize a great number of diverse surface materials. Interpretation of these very high-dimensional signatures, however, has proved an insurmountable challenge for man