Self-Organizing Maps (SOM) Modelling and Analysis
Visual analysis of self-organizing maps
... component analysis (CCA), auto-associative neural networks, neural scales, SAMANN, and self- organizing maps ...a self-organizing neural network that performs two tasks: vector ...
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Self Organizing Maps: Fundamentals
... The Adaptive Process Clearly our SOM must involve some kind of adaptive, or learning, process by which the outputs become self-organised and the feature map between inputs and outputs is formed. The point of the ...
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Self Organizing Maps for the Visual Analysis of Pitch Contours
... By enabling these interactions we present the analyst with the flexible possibilities for an itera- tive analysis process. The system first provides an overview of the data, the analyst is able to interact with ...
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Application of Self-Organizing Maps in Conformational Analysis of Lipids
... B. SOM Analysis. The SOM can be used to map n-dimensional input vectors to the neurons in a two-dimensional array, where the input vectors sharing common features end up on the same or neighboring neurons. 23,24 ...
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Conformational analysis of lipid molecules by self-organizing maps
... Leaving aside the details in different force fields used in the two studies, an important factor affecting the results of a SOM analysis is how the conformational space of the mol- ecules is sampled. In the ...
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Self organizing maps in computer security
... Some argue that biologically inspired algorithms are the future of solving diffi- cult problems in computer science. Others strongly believe that the future lies in the exploration of mathematical foundations of problems ...
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Initialization Issues in Self-organizing Maps
... present analysis and solutions to problems related to initial positioning of neurons in a classic self-organizing map (SOM) neural ...to self-similar ...a self-similar curve such as ...
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Fingerprint Matching with Self Organizing Maps
... Fax: +86-21-62489821 The Self-Organizing Map (SOM) is a neural network algorithm, which uses a competitive learning technique to train itself in an unsupervised manner. SOMs are different from other ...
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Information Visualization with Self-Organizing Maps
... handle and includes hardly any visualisation support. The other package is the SOM Toolbox [Vesanto et al (1999)]. The Toolbox is a software library for Matlab. It is strong in terms of map visualisation but performs ...
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Implementation of Self-Organizing Maps with Python
... data analysis community for some ...Component Analysis (PCA), a statistical procedure based on or- thogonal ...data analysis and the creation of predictive ...as Self-Organizing ...
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Self Organizing Maps for Visualization of Categories
... Categories are initially organized in tree-like structures that form hierarchies, with general abstract concepts in the higher parts of the tree. As some concepts can be related to more than one parent the relations ...
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Analysis of electric power consumption using Self-Organizing Maps.
... Dimension reduction algorithms (Kourti and MacGregor, 1995) have a huge potential in the study of electric systems and improvement of their energy efficiency. Normally, the number of electric variables involved in this ...
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Stock market series analysis using self-organizing maps
... The Self-Organizing Map (SOM) (Kohonen 1982) is an unsupervised neural-network algorithm with topology preservation. The powerful visualization techniques for SOM models result from the useful and unique ...
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EXPLORATORY GEOSPATIAL DATA ANALYSIS USING SELF-ORGANIZING MAPS
... DATA ANALYSIS USING SELF-ORGANIZING MAPS Case Study of Portuguese Mainland Regions Fernando ...the Self-Organizing Map (SOM) in visual exploration of physical geography ...
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Self-Organizing g Maps (SOM) COMP61021 Modelling and Visualization of High Dimensional Data
... – SOM is a biologically inspired unsupervised neural network that approximates an unlimited number of input data by a finite set of nodes arranged in a grid of low-dimension, where nei[r] ...
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Country corruption analysis with self organizing maps and support vector machines.
... SOM Analysis 4.1 Exploring the Data We started by training a self-organizing map of 15 by 15 neurons: with this size it can be expected that each neuron will be the BMU for at most a few observations ...
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Analysis and visualization of gene expression data using Self-Organizing Maps
... K-means clustering), less attention has been paid toward visualizing the data. There seems to be a common misunderstanding in the bioinformatics community that in SOM each map unit should be regarded as a separate ...
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Visual-Interactive Analysis With Self-Organizing Maps - Advances and Research Challenges
... ysis, as it not only provides the data reduction, but also a spatialization of cluster prototypes forming a baseline for visualization and interaction with the data. In this article, we survey applications of the SOM ...
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Using self organizing maps on compositional data
... The Self Organizing Maps (SOM) algorithm is an unsupervised neural network with properties of vector quantization and vector projection algorithms (Kohonen ...
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Face Recognition Using Self-Organizing Maps
... Kohonen self-organizing map is used as a feature ...Kohonen self-organizing map for codebook ...changes. Self-organizing map (SOM) is utilized to transform the high dimensional ...
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