Download Advances in Self-Organizing Maps: 7th International by Takashi Abe, Shigehiko Kanaya, Toshimichi Ikemura (auth.), PDF

By Takashi Abe, Shigehiko Kanaya, Toshimichi Ikemura (auth.), José C. Príncipe, Risto Miikkulainen (eds.)

This booklet constitutes the refereed court cases of the seventh overseas Workshop on Advances in Self-Organizing Maps, WSOM 2009, held in St. Augustine, Florida, in June 2009.

The forty-one revised complete papers offered have been conscientiously reviewed and chosen from a number of submissions. The papers care for themes within the use of SOM in lots of parts of social sciences, economics, computational biology, engineering, time sequence research, facts visualization and theoretical machine science.

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Quantization error for the NASA valve data set suitable to deal with time series data than static competitive neural networks. Furthermore, since the temporal SOMs had equivalent performances, preference is given to the Kangas’s model due to its implementational simplicity. The last experiment involves testing the Kangas’ model in a real-world time series data from the NASA shuttle program. The NASA valve data set [13] consists of solenoid current measurements recorded on Marrotta series MPV41 valves as they are remotely opened and closed in a laboratory.

Let T = (V, E, w) be a tree that is: (i) rooted, with v0 the root vertex, and (ii) ordered, which means that there is a Table 1. Notations and definitions G = (V, E, w) V = {v1 , v2 , . . vn } E = {e1 , e2 , . . em } w : E → R+ |V | |E| e = {vi , vj } dij = w (e) D = (dij )nn A graph Set of vertices Set of edges Function assigning a positive real number to an edge Degree of graph, cardinality of V Order of graph, cardinality of E Edge connecting vertices vi and vj Distance between vi and vj Distance matrix Clustering Hierarchical Data Using Self-Organizing Map 23 Fig.

Neural Networks 15(8-9), 945–952 (2002) 7. : Online algorithm for the self-organizing map of symbol strings. Neural Networks 17(8-9), 1231–1239 (2004) 8. : Generalizing self-organizing map for categorical data. IEEE Transactions on Neural Networks 17(2), 294–304 (2006) 9. : UCI Machine Learning Repository. edu/ml/datasets/Zoo 10. : Graphs, Networks and Algorithms. Algorithms and Computation in Mathematics, vol. 5. Springer, Berlin (English edition, 2002) 11. : Neural Networks. A Comprehensive Foundation, 2nd edn.

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