The self-organising map, robustness, self-organising criticality and power law
نویسنده
چکیده
منابع مشابه
Text Classification and Labelling of Document Clusters with Self-Organising Maps
The freely available law on the Internet could be one of the best application areas of text classification and labelling. This paper explores the high potential of the self-organising map for information reconnaissance by classifying and describing unknown legal text collections. The maps can be seen as topic-oriented libraries that are automatically created without intellectual input. The clus...
متن کاملDynamic self-organising map
6 We present in this paper a variation of the self-organising map algorithm where the original 7 time-dependent (learning rate and neighbourhood) learning function is replaced by a time8 invariant one. This allows for on-line and continuous learning on both static and dynamic 9 data distributions. One of the property of the newly proposed algorithm is that it does 10 not fit the magnification l...
متن کاملUnsupervised multimodal processing
We present two separate algorithms for unsupervised multimodal processing. Our first proposal, the singlepass Hebbian linked self-organising map network, significantly reduces the training of Hebbian-linked selforganising maps by computing in a single epoch the weights of the links associating the separate modal maps. Our second proposal, based on the counterpropagation network algorithm, imple...
متن کاملOn Document Classification with Self-Organising Maps
This research deals with the use of self-organising maps for the classification of text documents. The aim was to classify documents to separate classes according to their topics. We therefore constructed self-organising maps that were effective for this task and tested them with German newspaper documents. We compared the results gained to those of k nearest neighbour searching and k-means clu...
متن کاملA novel self-organising clustering model for time-event documents
Purpose Neural document clustering techniques, e.g., self-organising map (SOM) or growing neural gas (GNG), usually assume that textual information is stationary on the quantity. However, the quantity of text is ever-increasing. We propose a novel dynamic adaptive self-organising hybrid (DASH) model, which adapts to time-event news collections not only to the neural topological structure but al...
متن کامل