Publications by authors named "T Kohonen"

In coastal archipelago areas of the northern Baltic Sea, significantly higher phosphate concentrations (6.0±4.5μmol/l, mean±SD) were measured in water samples close to the sediment surface compared with those from 1m above the seafloor (1.

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The self-organizing map (SOM) is an automatic data-analysis method. It is widely applied to clustering problems and data exploration in industry, finance, natural sciences, and linguistics. The most extensive applications, exemplified in this paper, can be found in the management of massive textual databases and in bioinformatics.

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In this article, we introduce a software package that applies a corpus-based algorithm to derive semantic representations of words. The algorithm relies on analyses of contextual information extracted from a text corpus--specifically, analyses of word co-occurrences in a large-scale electronic database of text. Here, a target word is represented as the combination of the average of all words preceding the target and all words following it in a text corpus.

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This article describes the implementation of a system that is able to organize vast document collections according to textual similarities. It is based on the self-organizing map (SOM) algorithm. As the feature vectors for the documents statistical representations of their vocabularies are used.

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The Self-Organizing Map (SOM) algorithm was developed for the creation of abstract-feature maps. It has been accepted widely as a data-mining tool, and the principle underlying it may also explain how the feature maps of the brain are formed. However, it is not correct to use this algorithm for a model of pointwise neural projections such as the somatotopic maps or the maps of the visual field, first of all, because the SOM does not transfer signal patterns: the winner-take-all function at its output only defines a singular response.

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