The artificial neural network class of self-organizing maps (SOMs) is a powerful and promising cognitive modeling tool in the study of the brain and its disorders. Under this premise, this paper proposes a novel modification of the standard SOM algorithm in the form of an oscillating Topological Neighborhood (TN) width function. Existing research in neuroscience indicates that SOMs with oscilla...

Neural networks have been traditionally considered as an alternative approach to pattern recognition in general, and speech recognition in particular. There have been much success in practical pattern recognition applications using neural networks including multi-layer perceptrons, radial basis functions, and self-organizing maps (SOMs). In this paper, we propose a system of SOMs based on the a...

Active Contour Models (ACMs) constitute a powerful energy-based minimization framework for image segmentation, based on the evolution of an active contour. Among ACMs, supervised ACMs are able to exploit the information extracted from supervised examples to guide the contour evolution. However, their applicability is limited by the accuracy of the probability models they use. As a consequence, ...

Self-organizing maps (SOM) have been successfully applied in many fields of research. In this paper, we demonstrate the use of a neural-network-based tool for a data analysis in fluidized bed energy plants. The software is based on selforganizing maps. Reference vectors of SOMs can be classified by K-means algorithm into clusters, which represented different states of processes. The differences...

The purpose of this study is to implement the ensemble self-organizing maps (E-SOM) method impute missing values at preprocessing data stage, which an important stage when making predictions or classifications. Ensemble Self-Organizing Maps development SOM imputation method, in E-SOM implemented by applying framework using several SOMs improve generalization capabilities. In study, South Africa...

ABSRACT Since it takes time to do experiments in bioinformatics, biological datasets are sometimes small but with high dimensionality. From probability theory, in order to discover knowledge from a set of data, we have to have a sufficient number of samples. Otherwise, the error bounds can become too large to be useful. For the SOM (SelfOrganizing Map) algorithm, the initial map is based on the...

BACKGROUND Self-organizing maps (SOMs) have now been applied for a number of years to identify patterns in large datasets; yet, their application in the spatiotemporal domain has been lagging. Here, we demonstrate how spatialtemporal disease diffusion patterns can be analysed using SOMs and Sammon's projection. METHODS SOMs were applied to identify synchrony between spatial locations, to grou...

Self-organizing maps (SOMs) are a relative newcomer to synoptic climatology; the method itself has only been utilized in the field for around a decade. In this article, we review the major developments and climatological applications of SOMs in the literature. The SOM can be used in synoptic climatological analysis in a manner similar tomostother clustering methods.However, as the results froma...

Acknowledgements Firstly, thanks to my supervisor Tom Gedeon, and Dingyun Zhu for their recommendations and support on this project. Moreover, thanks to Uwe R. Zimmer for his suggestions about the technique of report writing. Finally, thanks to my family and my friends for their encouragements. Abstract The spherical SOM (SSOM) has been proposed in order to remove the " border effect " in conve...

The problem of correctly diagnosing different types of ailments has been tackled with different artificial intelligence techniques since its inception. Both heuristic and statistically based algorithms have been discussed in the past. In this paper we establish a comparison between one heuristic algorithm based on partial precedence and majority decision rules and two types of statistical ones:...