Self-organizing maps (SOM) are a powerful tool for detecting patterns in large, multi-dimensional data sets. Additional visualization techniques have been developed to support the user to gain insight into its structure. For complex data sets, even these techniques are not easily interpretable. Most of them consist of a grid where each cell contains a single value. Such a structure can be seen ...

We present a new neural classification model called Concurrent Self-Organizing Maps (CSOM), representing a winner-takes-all collection of small SOM networks. Each SOM of the system is trained individually to provide best results for one class only. We have considered two significant applications: face recognition and multispectral satellite image classification. For first application, we have u...

An interactive face retrieval system that uses selforganizing maps and user feedback is described. The system solves some problems of related content-based image retrieval systems: non-existence of trivial high-level human descriptions of the images and the gap between the high-level descriptions and the low-level features used to index the images.

We have developed a method that utilizes hypertext link information in image retrieval from the World Wide Web. The basis of the method consists of a set of basic relations that can take place between two images in the Web. Our method uses the SHA-1 message digest algorithm for dimension reduction by random mapping. The Web link features have then been used in creating a SelfOrganizing Map of i...

Unsupervised neural networks such as the Kohonen Self-Organizing Maps (SOM) have been widely used for searching natural clusters in multidimensional and massive data. One example where the data available for analysis can be extremely large is seismic interpretation for hydrocarbon exploration. In order to assist the interpreter in identifying characteristics of interest confined in the seismic ...

We propose a new type of Self-Organizing Map (SOM) that is based on discretizations of curved, non-euclidean spaces. As an introductory example, we brieey discuss \spherical SOMs" on tesselations of the sphere for the display of directional data. We then describe the construction of \hyperbolic SOMs", using regular tesselations of the hyperbolic plane, which is a non-euclidean space characteriz...

Various forms of the self-organizing map (SOM) have been proposed as models of cortical development [Choe Y., Miikkulainen R., (2004). Contour integration and segmentation with self-organized lateral connections. Biological Cybernetics, 90, 75-88; Kohonen T., (2001). Self-organizing maps (3rd ed.). Springer; Sirosh J., Miikkulainen R., (1997). Topographic receptive fields and patterned lateral ...

Thus far, the success of capturing and classifying temporal information with neural networks has been limited. Our methodology adds a spatio-temporal coupling to the Self-Organized Feature Map (SOFM) which creates temporally and spatially localized neighborhoods in the map. The spatio-temporal coupling is based on traveling waves of activity starting at each winning node which are naturally att...

This paper describes a method for identifying Ontology components by using Self-Organizing Maps. Our system represents the knowledge contained in a particular digital archive by assembling and displaying the ontologies components. This novel approach provides an alternative solution to the problem of classifying on-line information and retrieval, support mechanisms that explore domains, and all...