Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst’s expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial di...

Whileas the Kohonen Self Organizing Map shows an asymptotic level density following a power law with a magnification exponent 2/3, it would be desired to have an exponent 1 in order to provide optimal mapping in the sense of information theory. In this paper, we study analytically and numerically the magnification behaviour of the Elastic Net algorithm as a model for self-organizing feature map...

The Self-Organizing Map (Kohonen, 1997) is an effective and a very popular tool for data clustering and visualization. With this method, the input samples are projected into a low dimension space while preserving their topology. The samples are described by a set of features. The input space is generally a high dimensional space Rd. 2D or 3D maps are very often used for visualization in a low d...

organizing lobar pneumonia is a rare form of bronchiolitis obliterans organizing pneumonia.  herein, we report a rare case of organizing pneumonia involving lung, mediastinum and esophagus.  a 16-year-old girl was referred to our center with clinical signs and symptoms of dysphagia and weight loss.  the main abnormal radiologic and endoscopic findings were stricture of the lower third of esopha...

In this paper we extend the Hierarchical Self Organizing Maps model (HSOM ) to address the problem of learning topological drift under non stationary and noisy environments. The new model combines the capabilities of robustness against noise and, at the same time, the flexibility to adapt to the changing environment. We call this model RoFlex-HSOM. The RoFlex-HSOM model consists in a hierarchic...

Self-organizing neural networks endeavour to preserve the topology of an input space by means of competitive learning. There are diverse measures that allow to quantify how good is this topology preservation. However, most of them are not applicable to measure non-linear input manifolds, since they don't consider the topology of the input space in their calculation. In this work, we have modifi...

Improved Generalization by Iterative Methods Josef G oppert, Wolfgang Rosenstiel University of T ubingen, Sand 13, D-72076 T ubingen Abstract. Interpolation and self-organizing maps have proved to be powerful in several applications. These methods are able to combine the topology preservation of the self-organizing map (SOM) and real valued data evaluation as required in sensoring and monito...

Over the last decade or so, artificial neural networks (ANNs) have become one of the most promising tools formodelling hydrological processes such as rainfall runoff processes. However, the employment of a single model doesnot seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. For this reason, this study aims at de...

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 ...