Results 221 to 230 of about 30,395 (263)
Some of the next articles are maybe not open access.
Fusion of self-organizing map and granular self-organizing map for microblog summarization
Soft Computing, 2020In this paper, we have proposed a fusion of two architectures, self-organizing map and granular self-organizing map (SOM + GSOM), for solving the microblog summarization task where a set of relevant tweets are extracted from the available set of tweets.
Naveen Saini +3 more
openaire +1 more source
Parallelizing self-organizing maps
1997Several ways of parallelizing the self-organizing network (Kohonen maps) are studied on the BSP-like parallel machine model. Optimal number of processors and criteria for choosing the right task decomposition are presented. Theoretical results are verified in the PVM environment on a cluster of workstations.
David Strupl, Roman Neruda
openaire +1 more source
SPIE Proceedings, 2005
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
Yingxin Wu 0001, Masahiro Takatsuka
openaire +1 more source
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
Yingxin Wu 0001, Masahiro Takatsuka
openaire +1 more source
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 ...
openaire +1 more source
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
openaire +1 more source
Fusion of Self Organizing Maps
2007An important issue in data-mining is to find effective and optimal forms to learn and preserve the topological relations of highly dimensional input spaces and project the data to lower dimensions for visualization purposes. In this paper we propose a novel ensemble method to combine a finite number of Self Organizing Maps, we called this model ...
Carolina Saavedra +3 more
openaire +1 more source
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 ...
openaire +1 more source
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
openaire +2 more sources
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
openaire +2 more sources
SOM of SOMs: Self-organizing Map Which Maps a Group of Self-organizing Maps
2005This paper aims to propose an extension of SOMs called an “SOM of SOMs,” or SOM2, in which the mapped objects are self-organizing maps themselves. In SOM2, each nodal unit of the conventional SOM is replaced by a function module of SOM. Therefore, SOM2 can be regarded as a variation of a modular network SOM (mnSOM).
openaire +1 more source
Essentials of the self-organizing map
Neural Networks, 2013The self-organizing map (SOM) is an automatic data-analysis method. It is widely applied to clustering problems and data exploration in industry, finance, natural sciences, and linguistics. The most extensive applications, exemplified in this paper, can be found in the management of massive textual databases and in bioinformatics. The SOM is related to
openaire +2 more sources