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

This e-book 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 rigorously reviewed and chosen from various submissions. The papers take care of 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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Extra resources for Advances in Self-Organizing Maps: 7th International Workshop, WSOM 2009, St. Augustine, FL, USA, June 8-10, 2009. Proceedings

Sample text

2) Do the types of memory used by the temporal SOMs influence their performances in this task? For this purpose, we present temporal variants of the standard SOM, such as the Kangas’ model [9], TKM-Temporal Kohonen Map [10] and RSOM-Recursive SOM [11]. Then, the Fuzzy ART network [12], which is a static competitive model with an inherent mechanism of novelty detection, is described. All these algorithms were trained on-line and computer simulations carried out in order to compare their performances.

Additionally due to the risk of disturbing the network with such operations while online segmenting the image, we use a parallel evaluation scheme Fig. 1. The prototypes are added to a second network, which is an exact copy of the first one.

Br 2 Nokia Institute of Technology (INdT) SCS Bl. br Abstract. In this paper we evaluate competitive learning algorithms in the task of identifying anomalous patterns in time series data. The methodology consists in computing decision thresholds from the distribution of quantization errors produced by normal training data. These thresholds are then used for classifying incoming data samples as normal/abnormal. For this purpose, we carry out performance comparisons among five competitive neural networks (SOM, Kangas’ Model, TKM, RSOM and Fuzzy ART) on simulated and real-world time series data.

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