In this paper we examine how Self-Organizing Maps (SOMs) can be used in detecting and describing emergent ontological relations between semantic objects and object classes in a visual database. The ontological relations we have studied include co-existence, taxonomies of visual and semantic similarity and spatial relationships. The used database contains 2618 images, each of which belongs to on...

In this paper we demonstrate that the Self-Organizing Maps of Kohonen can be used as speech feature ex-tractors that are able to take temporal context into account. We have investigated two alternatives to use SOMs as such feature extractors, one based on tracing the location of highest activity on a SOM, the other on integrating the activity of the whole SOM for a period of time. The experimen...

The aim of this paper is to present a typology of career paths in France drawn up with the Kohonen algorithm and its extension to a clustering method of life history analysis based on the use of Self Organizing Maps (SOMs). Several methods have previously been presented for transforming qualitative into quantitative information so as to be able to apply clustering algorithms such as SOMs based ...

Unsupervised self-organizing maps (SOMs), as well as supervised learning by Learning Vector Quantization (LVQ) can be defined for string variables, too. Their computing becomes possible when the SOM and the LVQ algorithms are expressed as batch versions, and when the average over a list of symbol strings is defined to be the string that has the smallest sum of generalized distance functions fro...

We submitted to ozone treatment Triticum aestivum (common wheat) seeds severely contaminated by fungi. Fungi colonies developed when seeds were placed over malt agar medium in Petri dishes; Fusarium sp. and Alternaria sp. were identified. However, conventional colonies counting did not allow a clear assessment of the effect of ozone disinfection. We thus used self-organizing maps (SOMs) to perf...

This report presents a methodology for missing value imputation. The methodology is based on an ensemble of Self-Organizing Maps (SOM), which is weighted using Nonnegative Least Squares algorithm. Instead of a need for lengthy validation procedure as when using single SOMs, the ensemble proceeds straight into final model building. Therefore, the methodology has very low computational time while...

Neural network algorithms have been recently applied to construct Parton Distribution Function (PDF) parametrizations which provide an alternative to standard global fitting procedures. We propose a technique based on an interactive neural network algorithm using Self-Organizing Maps (SOMs). SOMs are a class of clustering algorithms based on competitive learning among spatially-ordered neurons....

On January 19, 1996 we published in the Internet a demo of how to use Self-Organizing Maps (SOMs) for the organization of large collections of full-text les. Later we added other newsgroups to the demo. It can be found at the address http://websom.hut../websom/. In the present paper we describe the main features of this system, called the WEBSOM, as well as some newer developments of it.

Recent developments with Self-Organizing Maps (SOMs) produced methods capable of clustering graph structured data onto a fixed dimensional display space. These methods have been applied successfully to a number of benchmark problems and produced state–of–the–art results. This paper discusses a limitation of the most powerful version of these SOMs, known as probability measure graph SOMs (PMGrap...

HNC Software, Inc. has developed a system called DOCUVERSE for visualizing the information content of large textual corpora. The system is built around two separate neural network methodologies: context vectors and self organizing maps. Context vectors (CVs) are high dimensional information representations that encode the semantic content of the textual entities they represent. Self organizing ...