Self-Organizing map (SOM) is a widely used tool to find clustering and also to visualize high dimensional data. Several spherical SOMs have been proposed to create a more accurate representation of the data by removing the “border effect”. In this paper, we compare several spherical lattices for the purpose of implementation of a SOM. We then introduce a 2D rectangular grid data structure for r...

This study explores the self-organizing neural network as a model of lexical and morphological acquisition. We examined issues of generalization, representation, and recovery in a multiple feature-map model as implemented in DISLEX (Miikkulainen, 1997). Our results indicate that self-organization and Hebbian learning are two important computational principles that can account for the psycholing...

This paper addresses the use of self–organizing maps for baseline construction in chromatograms. Unlike local techniques, the problem is seen in terms of global optimization: a straight and smooth path including sampled points with high significance for baseline membership is to be found. For their smoothing capabilities, and for reproducing the probability density function of the input, self–o...

Accurate discrimination between speech and non-speech is an essential part in many tasks of speech processing systems. In this paper an approach to the classification part of a Voice Activity Detector (VAD) is presented. Some possible shortcomings of presentVAD-systems are described and a classification approach which overcomes these weaknesses is derived. This approach is based on a Self-Organ...

Mapping techniques have been regularly used for visualization of high-dimensional data sets. In this paper, mapping to d 2 is studied, with the purpose of feature extraction. Two di erent non-linear techniques are studied: self-organizing maps and auto-associative feedforward networks. The non-linear techniques are compared to linear Principal Component Analysis (PCA). A comparison with respect...

Abstract This work in progress shows a method for classifying dendritic spines by their shape. Focal points are the extraction of features from three-dimensional spine data and the following classification of the spines. Hence there will be only little reflection of biological aspects of this problem. Feature extraction based on moments and spherical coordinates will be discussed. Furthermore, ...

The objective of this work is to develop a digitized mammograms’ feature extraction approach using Kohonen’s Self-Organizing Maps (SOM). Once developed, the SOM network will be used as the first processing stage in a breast cancer computer aided diagnosis (CAD) system. Its role will be to offer segmented data as input to a second stage dedicated to the diagnosis task, which will be implemented ...

We present a segmentation technique for image sequences using Self Organizing Feature Maps(SOFM). Our goal is to develop a method which can identify homogeneous regions in a frame to represent higher level objects for content based manipulation of image sequences. The proposed scheme extracts pixel based multiple features, such as motion and textures, and then, different weights are applied to ...

A modification of the well-known PicSOM retrieval system is presented. The algorithm is based on a variant of the self-organizing map algorithm that uses bootstrapping. In bootstrapping the feature space is randomly sampled and a series of subsets are created that are used during the training phase of the SOM algorithm. Afterwards, the resulting SOM networks are merged into one single network w...

In a wireless sensor network, appropriate use of resources can be achieved by choosing the most relevant sensors that are important to the current context. By applying feature selection to determine the optimal sensor locations, the number of sensors can be reduced and great savings (in term of power, hardware, and transmission channel) can be achieved without degrading the decision process. Th...