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

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 ebook constitutes the refereed lawsuits 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 offered have been rigorously reviewed and chosen from various submissions. The papers care for issues 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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Read Online or Download Advances in Self-Organizing Maps: 7th International Workshop, WSOM 2009, St. Augustine, FL, USA, June 8-10, 2009. Proceedings PDF

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

Example text

That is, some regions of the background are indicated as foreground and vice versa. Since the learning and recognition can be improved as the quality of the figure-ground segmentation is enhanced, we follow the concept of hypothesis refinement to derive the significant object parts (according to the underlying image features) from this initial guess. In [1] we investigated methods for object segmentation that use prototypical feature representatives to model figure and ground. In particular, we used binarized depth hypotheses as a supervised label for the image features to train a classifier for figure and ground with GLVQ.

The Fuzzy ART Algorithm: We also evaluate the performance of the Fuzzy ART algorithm [12] on anomaly detection in time series, due to its simplicity of implementation and low computational cost. The input vector x+ (t) is presented to a competitive layer of Q neurons. The winning neuron i∗ is selected if its choice function Ti∗ is the highest one among all neurons: i∗ (t) = arg max {Ti (t)} , ∀i (5) where the choice function Ti is computed as follows: Ti (t) = |x+ (t) ∧ wi (t)| , ε + |wi (t)| (6) where 0 < ε 1 is a very small constant, and |u| denotes the L1 -norm of the vector u.

Q. Recurrent SOM (RSOM): In this variant, a temporal smoothing mechanism acts over the difference vector d(t) = x+ (t) − wi (t): yi (t) = (1 − λ)yi (t − 1) + λd(t). (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.

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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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