Results of neural network learning are always subject to some variability, due to the sensitivity to initial conditions, to convergence to local minima, and, sometimes more dramatically, to sampling variability. This paper presents a set of tools designed to assess the reliability of the results of self-organizing maps (SOM), i.e. to test on a statistical basis the confidence we can have on the...

Self-Organizing Map (SOM) is a promising tool for exploring large multi-dimensional data sets. It is quick and convenient to train in an unsupervised fashion and, as an outcome, it produces natural clusters of data patterns. An example of application of SOM to the new OGLE-III data set is presented along with some preliminary results. Once tested on OGLE data, the SOM technique will also be imp...

The self-organizing map (SOM) methodology does vector quantization and clustering on the dataset, and then projects these clusters in a lower dimensional space, such as 2D map, by positioning similar clusters in locations that are spatially closer in the lower dimension space. This makes the SOM methodology an effective tool for data visualization. However, in a world where mined information fr...

Self-Organizing Map (SOM) algorithm has been extensively used for analysis and classification problems. For this kind of problems, datasets become more and more large and it is necessary to speed up the SOM learning. In this paper we present an application of the Simulated Annealing (SA) procedure to the SOM learning algorithm. The goal of the algorithm is to obtain fast learning and better per...

For a solution of the visual correspondence problem we have modified the Self Organizing Map (SOM) to map image planes onto another in a neighborhoodand feature-preserving way. We have investigated the convergence speed of this SOM and Dynamic Link Matching (DLM) on a benchmark problem for the solution of which both algorithms are good candidates. We show that even after careful parameter adjus...

In order to remove the “border effect”, several spherical Self-Organizing Maps (SOM) based on the geodesic dome have been proposed. However, existing neighborhood searching methods on the geodesic dome are much more time-consuming than searching on the normal rectangular/hexagonal grid. In this paper, we present detailed descriptions of the algorithms used in training the Geodesic SOM (GeoSOM),...

Models are abstractions of observed real world phenomena or processes. A good model captures the essential properties of the modeled phenomena. In the statistical learning paradigm the processes that generate observations are assumed unknown and too complex for analytical modeling, thus the models are trained from more general templates with measured observations. A substantial part of the proc...

A Self-Organizing Map (SOM) is an effective tool for clustering and visualizing high-dimensional complex data on a two-dimensional map. We modified the conventional SOM to genome informatics, making the learning process and resulting map independent of the order of data input, and developed a novel bioinformatics tool for phylogenetic classification of sequence fragments obtained from pooled ge...

The Self-Organizing Map (SOM) is a widely used data visualization tool in engineering applications. The algorithm performs a non-linear mapping from a highdimensional data space to a low-dimensional space, which is typically a two-dimensional, rectangular grid. This makes it possible to present multidimensional data in two dimensions. Often the model vectors of the SOM and a new data sample nee...

The Self-Organizing Map, SOM, is a widely used tool in exploratory data analysis. A major drawback of the SOM has been the lack of a theoretically justified criterion for model selection. Model complexity has a decisive effect on the reliability of visual data analysis, which is a main application of the SOM. In particular, independence of variables cannot be observed unless generalization of t...