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 ...

We propose a method for inferring semantic information from textual data in content-based multimedia retrieval. Training examples of images and videos belonging to a specific semantic class are associated with their low-level visual and aural descriptors augmented with textual features such as frequencies of significant words. A fuzzy mapping of a semantic class in the training set to a class o...

Self-Organizing Maps (SOMs) are a good method to cluster and visualize large collections of text documents, but they are computationally expensive. In this paper, we investigate ways to use natural language parsing of the texts to remove unimportant terms from the usual bag-of-words representation, to improve efficiency. We find that reducing the document representation to just the heads of nou...

In this paper, we propose a method of reducing topological defects in self-organizing maps (SOMs) using multiple scale neighborhood functions. The multiple scale neighborhood functions are inspired by multiple scale channels in the human visual system. To evaluate the proposed method, we applied it to the traveling salesman problem (TSP), and examined two indexes: the tour length of the solutio...

This paper presents a new approach to flood mapping using satellite synthetic-aperture radar (SAR) images that is based on intelligent techniques. In particular, we apply artificial neural networks, self-organizing Kohonen’s maps (SOMs), for SAR image segmentation and classification. Our approach was used to process data from different satellite SAR instruments (ERS-2/SAR, ENVISAT/ASAR, RADARSA...

Improved methods are presented for learning metrics that measure only important distances. It is assumed that changes in primary data are relevant only to the extent that they cause changes in auxiliary data, available paired with the primary data. The metrics are here derived from estimators of the conditional density of the auxiliary data. More accurate estimators are compared, and a more acc...

Linear models are ususally preferable due to their simplicity. However, nonlinear models often emerge in practice. A popular approach for dealing with nonlinearities is using a piecewise-linear approximation. In such context, inspired from both Fuzzy Inference Systems (FISs) of TSK type and Self-Organizing Maps (SOMs), this work introduces enhancements based on Interval Numbers and, ultimately,...

In the online version of Self-Organizing Maps, the results obtained from different instances of the algorithm can be rather different. In this paper, we explore a novel approach which aggregates several results of the SOM algorithm to increase their quality and reduce the variability of the results. This approach uses the variability of the algorithm that is due to different initialization stat...

In this paper we present the FPGA-based hardware accelerator gNBXe for emulation of classical Self-Organizing Maps (SOMs) and Conscience SOM (CSOM) in a multi-FPGA environment. After discussing how the CSOM is mapped to a resource-efficient digital hardware implementation, we present how the modular system architecture can be flexibly adapted to various application datasets. The hardware costs ...