Learn More
When used for visualization of high-dimensional data, the self-organizing map (SOM) requires a coloring scheme, such as the U-matrix, to mark the distances between neurons. Even so, the structures of the data clusters may not be apparent and their shapes are often distorted. In this paper, a visualization-induced SOM (ViSOM) is proposed to overcome these(More)
A self-organizing mixture network (SOMN) is derived for learning arbitrary density functions. The network minimizes the Kullback-Leibler information metric by means of stochastic approximation methods. The density functions are modeled as mixtures of parametric distributions. A mixture needs not to be homogenous, i.e., it can have different density(More)
Efficient exploitation of spatial diversity is fundamentally important to resource critical wireless applications [Tsoulos (1999)]. In this paper, we first study the performance of intelligent scheduling for space-division multiple-access (SDMA) wireless networks [Suard (1998), Farsakh (1998)]. Based on the existing scheme, we propose a new medium access(More)
The self-organising map (SOM) has been successfully employed as a nonparametric method for dimensionality reduction and data visualisation. However, for visualisation the SOM requires a colouring scheme to imprint the distances between neurons so that the clustering and boundaries can be seen. Even though the distributions of the data and structures of the(More)
  • Hujun Yin
  • Computational Intelligence: A Compendium
  • 2008
For many years, artificial neural networks (ANNs) have been studied and used to model information processing systems based on or inspired by biological neural structures. They not only can provide solutions with improved performance when compared with traditional problem-solving methods, but also give a deeper understanding of human cognitive abilities.(More)
The self-organising map (SOM) is finding more and more applications in a wide range of fields, such as clustering, pattern recognition and visualisation. It has also been employed in knowledge management and information retrieval. We propose an alternative to existing 2-dimensional SOM based methods for document analysis. The method, termed Adaptive(More)
The kernel method has become a useful trick and has been widely applied to various learning models to extend their nonlinear approximation and classification capabilities. Such extensions have also recently occurred to the Self-Organising Map (SOM). In this paper, two recently proposed kernel SOMs are reviewed, together with their link to an energy(More)
Time course measurements are becoming a common type of experiment in the use of microrarrays. The temporal order of the data and the varying length of sampling intervals are important and should be considered in clustering time-series. However, the shortness of gene expression time-series data limits the use of conventional statistical models and techniques(More)