Results 211 to 220 of about 25,182 (261)
Advanced Functional Materials, EarlyView.Polarity‐matched Zr–MOFs program Nafion's nanoscale morphology during solution casting. Hydrophilic UiO‐66 preserves hydrated pathways, while hydrophobic UiO‐67 increases the proton hopping sites by densifying ionic clusters. Combining both fillers yields a membrane that delivers 176 mS cm−1 conductivity, reaches 1.46 W cm−2 under 200 kPa, and triples ...
Yonghwi Cho +13 more
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Proceedings of the IEEE, 1990
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TEUVO Kohonen
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
TEUVO Kohonen
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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
TEUVO Kohonen
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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.
Sriparna Saha +2 more
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The diffuse self-organizing map
SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483), 2004This paper proposes a new diffuse self-organizing map (DSOM), which is a competitive self-organizing neural network that can forms a topological map of the input vector space in a circle-shaped region. Active and inactive neurons are introduced to restrict the range of competition and the size of the final map.
Yi Wang +3 more
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Clustering of the self-organizing map
IEEE Transactions on Neural Networks, 2000The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar ...
Juha Vesanto, Esa Alhoniemi
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Multinomial Self Organizing Maps
2010 10th International Conference on Intelligent Systems Design and Applications, 2010Co-occurrence data matrices arise frequently in various important applications such as a document clustering. By considering a multinomial mixture model, we present a new probabilistic Self-Organizing Map (SOM) for clustering and visualizing this kind of data. Contrary to SOM, our proposed learning algorithm optimizes an objective function.
Faryel Allouti +2 more
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Asynchronous self-organizing maps
IEEE Transactions on Neural Networks, 2000A recently defined energy function which leads to a self-organizing map is used as a foundation for an asynchronous neural-network algorithm. We generalize the existing stochastic gradient approach to an asynchronous parallel stochastic gradient method for generating a topological map on a distributed computer system (MIMD).
Maurice W. Benson, Jie Hu
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The chaotic self-organizing map
Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, 2002A chaotic self-organizing map can be produced by replacing the linear neural units of the conventional self-organizing map with neural units capable of producing chaos. The introduction of chaos into the self-organizing map is shown to improve the ability of the network to cluster input patterns. >
Alison A. Dingle +2 more
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Self-organized criticality and the self-organizing map
Physical Review E, 2001The self-organizing map (SOM), a biologically inspired, learning algorithm from the field of artificial neural networks, is presented as a self-organized critical (SOC) model of the extremal dynamics family. The SOM's ability to converge to an ordered configuration, independent of the initial state, is known and has been demonstrated, in the one ...
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