The self-organizing feature maps developed by Kohonen appear to capture some of the advantages of the natural systems on which they are based. A summary of the operation of this form of artificial neural network is presented. It was concluded that the primary benefits of using self-organizing feature maps result from their adaptability and plasticity while most problems are largely caused by th...

We present a framework in which self-organizing systems can be used to perform change of representation on knowledge discovery problems, to learn from very large databases. Clustering using self-organizing maps is applied to produce multiple, intermediate training targets that are used to define a new supervised learning and mixture estimation problem. The input data is partitioned using a stat...

The spoken speech is the easiest and most natural way for the communication between human beings. So, the human-machine communication can be executed based on the way that human-human communication occurs. Researches in automatic speech recognition (ASR) have been developed for decades to produce communication as natural as possible. There some few attempts to use Self-organizing Maps to solve ...

The method of self-organizing maps (SOM) is a method of exploratory data analysis used for clustering and projecting multi-dimensional data into a lower-dimensional space to reveal hidden structure of the data. The algorithm used retains local similarity and neighborhood relations between the data items. In some cases we have to compare the structure of data items visualized on two or more self...

the aim of this work is to use self organizing map (som) for clustering of locomotion kinetic characteristics in normal and parkinson’s disease. the classification and analysis of the kinematic characteristics of human locomotion has been greatly increased by the use of artificial neural networks in recent years. the proposed methodology aims at overcoming the constraints of traditional analysi...

With rapid advances in genomics, phylogenetics has turned to phylogenomics due to the availability of large amounts of sequence and genome data. However, incongruence between species trees and gene trees remains a challenge in molecular phylogenetics for its biological and algorithmic complexities. A state-of-the-art gene concatenation approach was proposed to resolve this problem by inferring ...

In recent years, the proliferation of multi-temporal census data products and the increased capabilities of geospatial analysis and visualization techniques have encouraged longitudinal analyses of socioeconomic census data. Traditional cartographic methods for illustrating socioeconomic change tend to rely either on comparison of multiple temporal snapshots or on explicit representation of the...

In this contribution we present two experimental scenarios in which we employed Self Organizing Maps (SOMs) to detect affective states. The first scenario is related towards designing a “Personal Stress Prevention Assistant”: we summarize our efforts to detect affective information related to stress in the posture channel. We show that a person-independent discrimination of stress from cognitiv...

This paper presents how Self-Organizing Maps and especially Kohonen maps can be applied to digital images of ancient collections in the perspective of valorization and diffusion. As an illustration, a scheme of transparency reduction of the digitized Gutenberg Bible is presented. In this two steps method, the Kohonen map is trained to generate a set of test vectors that will train in a supervis...