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

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

ISBN-10: 3642023967

ISBN-13: 9783642023965

ISBN-10: 3642023975

ISBN-13: 9783642023972

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

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

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Extra info for Advances in Self-Organizing Maps: 7th International Workshop, WSOM 2009, St. Augustine, FL, USA, June 8-10, 2009. Proceedings

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W : E → 1 ), though it can be any positive real number and different for each edge. The paper calculates the distances of all pairs of vertices by using a levelorder traversal of the tree, and thus derives a distance matrix that makes up the numerical representation of the zoo-dataset. Third, for each representation of the zoo-dataset, original binary encoding and the paper’s graph-theoretical approach, the paper uses bootstrap to draw one hundred random samples with replacement. Fourth, for each bootstrapped sample, the paper trains two SOMs, batch and sequence, with a Guassian neighborhood and an 8 x 5 hexagonal lattice.

Cottrell et al. The next two subsections detail how to perform the GLM regression on the engine variables, and how to use the Self-Organizing Maps on the GLM residuals. 1 Computation of the Residuals (So-Called Corrected Values) The computation of the values obtained by removing the effects of the environment variables and of the engine is done by using a General Linear Model, where the explanatory variables are of two kinds: one variable is categorical (the engine number), the others are real-valued variables (the environment variables).

From the resulting quantization errors {eq (i∗ , t)}Tt=1 training vectors, we compute decision thresholds for the anomaly detection tests. e. not anomalous) behavior of the input variable whose time series model is being constructed. In order to compute decision thresholds we apply the method proposed in [2]. g. 01), we are interested in an interval within which we can certainly find a percentage of 100(1−α)% of normal values of the quantization error. We then proceed with the computation of the lower and upper boundaries of the detection interval as follows: – Lower Limit (τ − ): This is the 100 α2 th percentile1 of the distribution of quantization errors associated with the training data vectors.

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Advances in Self-Organizing Maps: 7th International Workshop, WSOM 2009, St. Augustine, FL, USA, June 8-10, 2009. Proceedings by Takashi Abe, Shigehiko Kanaya, Toshimichi Ikemura (auth.), José C. Príncipe, Risto Miikkulainen (eds.)


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