Self-Organizing Maps (SOMs) have been used to visualize tradeoffs of Pareto solutions in the objective function space for engineering design obtained by Evolutionary Computation. Furthermore, based on the codebook vectors of cluster-averaged values of respective design variables obtained from the SOM, the design variable space is mapped onto another SOM. The resulting SOM generates clusters of ...

The application of hierarchical self organizing maps (HSOM) to the segmentation of cell migration images, obtained during high-content screening in molecular medicine, is described. The segmentation is critical to our larger project for developing methods for the automatic annotation of cell migration images. The HSOM appears to perform better than the conventional computervision methods of his...

Next-generation sequencing techniques produce an enormous amount of sequence data. Analyzing these sequences requires an efficient method that can handle large amounts of data. Self-organizing maps (SOMs), which use the frequencies of N-tuples and correlation coefficients of nucleotide, can categorize sets of DNA sequences with unsupervised learning. And Pareto learning SOM can classify the DNA...

Cluster analysis is the most important method for analyzing large-scale gene expression patterns. The matrix representation of microarray data and its successive ‘optimal’ incisional hyperplanes that create topdown hierarchical tree are a useful platform for developing optimization algorithms to determine the ‘optimal’ clusters from a pairwise proximity matrix which represents completely connec...

Organizations in today’s software industry are increasingly faced with the challenge of managing information about their past, present, and future projects. The effective and efficient reuse of past knowledge, experience, and assets is one of the key success factors in the software business. To organize the huge number of documents arising during software projects, e. g. use case documents, a d...

In this paper classification of surface defects is considered. The classification system consists of several classifiers whose outputs are combined in order to produce the final classification. The self-organizing maps (SOMs) are used as classifiers. Each SOM is taught unsupervised with examples of defects. Classification is based on the internal structure and the shape characteristics of defec...

It has been proved that in one-dimensional cases, the weights of Kohonen’s self-organizing maps (SOM) will become ordered with probability 1; once the weights are ordered, they cannot become disordered in future training. It is difŽcult to analyze Kohonen’s SOMs in multidimensional cases; however, it has been conjectured that similar results seem to be obtainable in multidimensional cases. In t...

Inspired by the oscillatory nature of cerebral cortex activity, we recently proposed and studied self-organizing maps (SOMs) based on limit cycle neural activity in an attempt to improve the information efficiency and robustness of conventional single-node, single-pattern representations. Here we explore for the first time the use of limit cycle SOMs to build a neural architecture that controls...

Using a distortion measure for the states emerging in self-organizing maps (SOMs) we mathematically analyse a recently proposed high-dimensional map formation model for ocular dominance patterns. We calculate critical values of parameters for ocular dominance states to occur, and we determine how the pattern layout depends on these parameters. The analysis reveals an increase of ocular dominanc...

Self-Organizing Maps (SOMs) have been successfully applied to content-based image retrieval (CBIR). In this study, we investigate the potential of PicSOM, an image database browsing system, applied to remote sensing images. Databases of small images were artificially created, either from a single satellite image for object detection, or two satellite images when considering change detection. By...