1. INTRODUCTION Self-organizing neural networks represent a family of useful clustering-based classification methods in several application domains. One such technique is the Kohonen Self-Organizing Feature Map (SOM) (Kohonen,

It is often reported in the technique literature that the success of the self-organizing feature map (SOM) formation is critically dependent on the initial weights and the selection of main parameters of the algorithm, namely, the learning-rate parameter and the neighborhood set. In this paper, we propose a healing mechanism to repair feature maps that are not well topologically ordered. The he...

In this paper, we consider how to represent world knowledge using the self-organizing map (SOM), how to use a simple recurrent network (SRN) to device sentence comprehension, and how to use the SOM output space to represent situations and facilitate grounded logical reasoning.

The Self-Organizing Map (SOM) is a powerful neural network method for the analysis and visualisation of high-dimensional data. In the Entire project, a data mining tool using the SOM was implemented and used to analyse world pulp and paper technology.

The Self-Organizing Map (SOM) can be used in implementing relevance feedback in an information retrieval system. In our approach, the map surface is convolved with a window function in order to spread the responses given by a human user for the seen data items. In this paper, a number of window functions with different sizes are compared in spreading positive and negative relevance information ...

Few existing methods enable the visualization of relationships between regulatory genomic activities and genome organization as captured by Hi-C experimental data. Genome-wide datasets are often displayed using “heatmap” matrices, but it is difficult to intuit from these heatmaps which biochemical compartmentalized together. High-dimensional data vectors can alternatively be projected onto thre...

The rapid proliferation of textual and multimedia online databases, digital libraries, Internet servers, and intranet services has turned researchers' and practitioners' dream of creating an information-rich society into a nightmare of info-gluts. Many researchers believe that turning an info-glut into a useful digital library requires automated techniques for organizing and categorizing large-...

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...

For unsupervised sequence processing, standard self organizing maps (SOM) can be naturally extended by recurrent connections and explicit context representations. Known models are the temporal Kohonen map (TKM), recursive SOM, SOM for structured data (SOMSD), and HSOM for sequences (HSOM-S). We discuss and compare the capabilities of exemplary approaches to store different types of sequences. A...

PROPRE is a generic and semi-supervised neural learning paradigm that extracts meaningful concepts of multimodal data flows based on predictability across modalities. It consists on the combination of two computational paradigms. First, a topological projection of each data flow on a self-organizing map (SOM) to reduce input dimension. Second, each SOM activity is used to predict activities in ...