This paper presents an enhancement made to a high dimensional variant of a growing self organizing map called the High Dimensional Growing Self Organizing Map (HDGSOM) that enhances the clustering of the algorithm. The enhancement is based on randomness that expedites the self organizing process by moving the inputs out from local minima producing better clusters within a shorter training time....

In a recent paper, T. Kohonen and P. Somervuo have shown that self-organizing maps (SOMs) are not restricted to numerical data. They can also be defined for symbol strings, provided that one defines an average function for strings and that the adaptation process is performed off-line (batch). In this paper, we present two different methods for computing averages of strings, as well as an on-lin...

The traditional approaches of classification are always unfavorable in the description of information distribution. This paper describes the BP neural network approach and the Kohonen neural network approach to the classification of remote sensing images. Two algorithms have their own traits and can be good used in the classification. A qualitative comparison demonstrates that both original ima...

In this paper we address the problem of analyzing and managing complex dynamic scenes captured in video. We present an approach to summarize video datasets by analyzing the trajectories of objects within them. Our work is based on the identification of nodes in these trajectories as critical points that describe the behavior of an object over a video segment. The time instances that correspond ...

We analyze a set of data describing N=3300 yeast genes, each gene characterized by d=13 variables (traits). First we performed an explorative data analysis and stated a high multivariate kurtosis. Next we clustered the data and visualized them using Kohonen’s self-organizing maps. This permitted us to get an idea how the data are distributed in the multivariate space.

This paper proposes a novel approach for a Constructive Self-Organizing Map (SOM) based world modeling for search and rescue operations in disaster environments. In our approach, nodes of the self organizing network consist of victim and waypoint classes where victim denotes a human being waiting to be rescued and waypoint denotes a free space that can be reached from the entrance of debris. Th...

Speech Perception of humans begins to develop as young as 6month-old or even earlier. The development of perception was suggested to be a self-organizing process driven by the linguistic environment to the infants [1]. Self-organizing maps have been widely used for modeling the perception development of infants [2]. However, in these models, temporal information within speech is ignored. Only s...

The present paper proposes an application of potentiality learning to supervised learning. The potentiality has been developed as a measure of the importance of components in the self-organizing maps (SOM) to extract important input neurons. The main characteristics lies in its simplicity and thus it can be easily implemented. If it is possible to use it for conventional supervised learning, be...

A system is described which takes synergies extracted from human grasp experiments and maps these onto a robot vision and hand-arm platform to facilitate the transfer of skills [1]. This system forms part of a framework which is extended by adding a self organizing map based affordance learning system. This affordance system learns the correlations between perceived object features and relevant...

A new family of self-organizing maps, the Winner-Relaxing Kohonen Algorithm, is introduced as a generalization of a variant given by Kohonen in 1991. The magnification behaviour is calculated analytically. For the original variant a magnification exponent of 4/7 is derived; the generalized version allows to steer the magnification in the wide range from exponent 1/2 to 1 in the one-dimensional ...