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Adaptive and Natural Computing Algorithms: 8th International by Sarunas Raudys (auth.), Bartlomiej Beliczynski, Andrzej PDF

By Sarunas Raudys (auth.), Bartlomiej Beliczynski, Andrzej Dzielinski, Marcin Iwanowski, Bernardete Ribeiro (eds.)

ISBN-10: 3540715908

ISBN-13: 9783540715900

ISBN-10: 3540716297

ISBN-13: 9783540716297

The quantity set LNCS 4431 and LNCS 4432 constitutes the refereed complaints of the eighth overseas convention on Adaptive and common Computing Algorithms, ICANNGA 2007, held in Warsaw, Poland, in April 2007.

The 178 revised complete papers awarded have been conscientiously reviewed and chosen from a complete of 474 submissions. The ninety four papers of the 1st quantity are equipped in topical sections on evolutionary computation, genetic algorithms, particle swarm optimization, studying, optimization and video games, fuzzy and tough structures, simply as category and clustering. the second one quantity includes eighty four contributions concerning neural networks, help vector machines, biomedical sign and photograph processing, biometrics, desktop imaginative and prescient, in addition to to regulate and robotics.

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Read or Download Adaptive and Natural Computing Algorithms: 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceedings, Part II PDF

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Additional info for Adaptive and Natural Computing Algorithms: 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceedings, Part II

Example text

On account of their specificity, expressed in the presence of two interconnected outputs, a notion of a basic operation orthogonal neuron (BOON) was proposed in [7] along with formulas for weights adaptation and error backpropagation: ∂E ∂u ∂E ∂w (n−1) e1 (n−1) e2 (n) = v1 v2 e · 1(n) , v2 −v1 e2 (n) = P2T · e1 (n) , e2 (n) ∂E e = v2 , −v1 · 1(n) , ∂t e2 (n−1) e1 (n−1) e2 (7) (8) (9) (n) = P1T · e1 (n) , e2 (10) (n) T ] where v1 and v2 are the inputs of the basic operation, the vector [ e(n) 1 , e2 refers to error values propagated back from the next layer and the vector (n−1) T [ e(n−1) ] defines the error values to propagate back to the previous , e2 1 one.

Kawaguchi In this paper, first, we analyze the asymmetric network with second order nonlinearity, which is based on the catfish retina for the detection of the changing and movement stimulus. We show the asymmetric network has the powerful ability in the detection of the changing stimulus and movement. Next, we present the network model developed by Simoncelli and Heeger [16], which shows first a linear receptive field, followed by half-squaring rectification and normalization in V1 and next V1 afferents, followed by half-squaring rectification and normalization.

In the first variant the DCT coefficients may be computed once and then used for classifier training. Using the neural feature extractor means that the data must be processed during every epoch to allow its weights adaptation. On Feature Extraction Capabilities of Fast Orthogonal Neural Networks 35 Fig. 7. Data points in the 2-dimensional feature space for FONN+MLP. The separating hyperplanes have been marked manually. There are many factors potentially influencing the overall efficiency, such as the type and implementation of the orthogonal transform or the dataset characteristics.

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Adaptive and Natural Computing Algorithms: 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceedings, Part II by Sarunas Raudys (auth.), Bartlomiej Beliczynski, Andrzej Dzielinski, Marcin Iwanowski, Bernardete Ribeiro (eds.)

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