Results 281 to 290 of about 7,151,068 (329)
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Denoising Autoencoder Self-Organizing Map (DASOM)
Neural Networks, 2018In this report, we address the question of combining nonlinearities of neurons into networks for modeling increasingly varying and progressively more complex functions.
C. Ferles +2 more
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Biological Cybernetics, 1989
Self-organized formation of topographic maps for abstract data, such as words, is demonstrated in this work. The semantic relationships in the data are reflected by their relative distances in the map. Two different simulations, both based on a neural network model that implements the algorithm of the selforganizing feature maps, are given.
Ritter, Helge, Kohonen, Teuvo
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Self-organized formation of topographic maps for abstract data, such as words, is demonstrated in this work. The semantic relationships in the data are reflected by their relative distances in the map. Two different simulations, both based on a neural network model that implements the algorithm of the selforganizing feature maps, are given.
Ritter, Helge, Kohonen, Teuvo
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Financial Self-Organizing Maps
2014This paper introduces Financial Self–Organizing Maps (FinSOM) as a SOM sub–class where the mapping of inputs on the neural space takes place using functions with economic soundness, that makes them particularly well–suited to analyze financial data. The visualization capabilities as well as the explicative power of both the standard SOM and the FinSOM ...
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Probabilistic PCA Self-Organizing Maps
IEEE Transactions on Neural Networks, 2009In this paper, we present a probabilistic neural model, which extends Kohonen's self-organizing map (SOM) by performing a probabilistic principal component analysis (PPCA) at each neuron. Several SOMs have been proposed in the literature to capture the local principal subspaces, but our approach offers a probabilistic model while it has a low ...
Ezequiel López-Rubio +2 more
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Recursive self-organizing maps
Neural Networks, 2002This paper explores the combination of self-organizing map (SOM) and feedback, in order to represent sequences of inputs. In general, neural networks with time-delayed feedback represent time implicitly, by combining current inputs and past activities.
exaly +4 more sources
Comparing self-organizing maps
1996In exploratory analysis of high-dimensional data the self-organizing map can be used to illustrate relations between the data items. We have developed two measures for comparing how different maps represent these relations. The other combines an index of discontinuities in the mapping from the input data set to the map grid with an index of the ...
Samuel Kaski, Krista Lagus
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Self-organizing Map Initialization
2005The solution obtained by Self-Organizing Map (SOM) strongly depends on the initial cluster centers. However, all existing SOM initialization methods do not guarantee to obtain a better minimal solution. Generally, we can group these methods in two classes: random initialization and data analysis based initialization classes.
Attik, Mohammed +2 more
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Self-Organizing Maps for imprecise data
Fuzzy Sets and Systems, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
D'URSO, Pierpaolo +2 more
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The Self-Organizing Map of Trees
Neural Processing Letters, 1998In the standard version of the Self-Organizing Map, each neuron is associated with a vector. An extension using trees instead of vectors is presented. Compared to vectors, trees provide remarkably more degrees of freedom. The essential points of self-organization, the distance function and the learning rule, are adapted to trees by means of graph ...
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2002
Self Organizing Features Maps are used for a variety of tasks in visualization and clustering, acting to transform data from a highdimensional original feature space to a (usually) two-dimensional grid. SOFMs use a similarity metric in the input space, and this composes individual feature differences in a way that is not always desirable.
Kennedy, Richard Lee, Hunter, Andrew
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Self Organizing Features Maps are used for a variety of tasks in visualization and clustering, acting to transform data from a highdimensional original feature space to a (usually) two-dimensional grid. SOFMs use a similarity metric in the input space, and this composes individual feature differences in a way that is not always desirable.
Kennedy, Richard Lee, Hunter, Andrew
openaire +2 more sources