The aim of this paper is to evaluate the financial performance of telecommunications companies with the help self-organising maps. A total of 88 companies is evaluated and benchmarked on the basis of seven key ratios, calculated for five consecutive years 1995-99. To conduct benchmarking with several financial key figures in a changing business environment is difficult and time consuming for to...

Our visualisation of the IEEE InfoVis citation network is based on 3D graph visualisation techniques. To make effective use of the third dimension we use a layered approach, constraining nodes to lie on parallel planes depending on parameters such as year of publication or link degree. Within the parallel planes nodes are arranged using a fast force-directed layout method. A number of clusters ...

Among current foreground detection algorithms for video sequences, methods based on self-organizing maps are obtaining a greater relevance. In this work we propose a probabilistic self-organising map based model, which uses a uniform distribution to represent the foreground. A suitable set of characteristic pixel features is chosen to train the probabilistic model. Our approach has been compare...

We compare the performance of ve algorithms for vector quan-tisation and clustering analysis: the Self-Organising Map (SOM) and Learning Vector Quantization (LVQ) algorithms of Kohonen, the Linde-Buzo-Gray (LBG) algorithm, the MultiLayer Perceptron (MLP) and the GMM/EM algorithm for Gaussian Mixture Models (GMM). We propose that the GMM/EM provides a better representation of the speech space an...

We propose a technique for the segmentation of images using both colour and texture information. This information is in the form of lowpass colour responses and sixteen Gabor texture lter responses. A self-organising feature map is trained to label any set of responses into appropriate categories. We quantify the success of the approach and assess the importance of colour and texture in the seg...