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

This booklet constitutes the refereed complaints 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 provided have been conscientiously reviewed and chosen from quite a few submissions. The papers care for issues within the use of SOM in lots of parts of social sciences, economics, computational biology, engineering, time sequence research, info visualization and theoretical computing device science.

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The residuals are then used as inputs to a Self-Organizing Map for the easy visualization of trajectories. Keywords: aircraft engine maintenance, fault detection, general linear models, self-organizing maps. 1 Introduction Security issues in the aircrafts are a major concern for obvious reasons. Among the many aspects of security issues, ensuring a proper operation of engines over their lifetime is an important task. C. Príncipe and R. ): WSOM 2009, LNCS 5629, pp. 37–44, 2009. © Springer-Verlag Berlin Heidelberg 2009 38 M.

11) The winning neuron is then redefined as i∗ (t) = arg min{yi (t)}, ∀i (12) and the learning rule in Eq. (3) is rewritten as wi (t + 1) = wi (t) + η(t)h(i∗ , i; t)yi (t), (13) where the memory is now taken into account when updating the weights of the winning neuron. We set yi (0) = 0, ∀i. 3 Detection Methodology Unlike the Fuzzy ART algorithm, the SOM-based methods previously described do not have an intrinsic mechanism to detect anomalous data. A. Barreto and L. Aguayo as a measure of the degree of proximity of x+ (t) to a statistical representation of normal behavior encoded in the weight vectors of the SOM variants.

One main criterion used for unsupervised setups is the distance of the features to their representatives, namely the quantization error. The criterion in Growing Neural Gas [4] (and similar for the Growing Cell structures [2]) aims at a minimization of the quantization error and introduces new prototypes where the quantization error is large, guaranteeing that the introduction of new prototypes reduces this error. Supervised LVQ primarily aims at the minimization of the classification error which offers another source of information.

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