Download PDF by Konstantinos Diamantaras, Wlodek Duch, Lazaros S. Iliadis: Artificial Neural Networks - ICANN 2010: 20th International

By Konstantinos Diamantaras, Wlodek Duch, Lazaros S. Iliadis

ISBN-10: 3642158218

ISBN-13: 9783642158216

This 3 quantity set LNCS 6352, LNCS 6353, and LNCS 6354 constitutes the refereed court cases of the 20 th overseas convention on man made Neural Networks, ICANN 2010, held in Thessaloniki, Greece, in September 2010. The 102 revised complete papers, sixty eight brief papers and 29 posters awarded have been conscientiously reviewed and chosen from 241 submissions. the second one quantity is split in topical sections on Kernel algorithms – help vector machines, wisdom engineering and choice making, recurrent ANN, reinforcement studying, robotics, self organizing ANN, adaptive algorithms – structures, and optimization.

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Download PDF by Konstantinos Diamantaras, Wlodek Duch, Lazaros S. Iliadis: Artificial Neural Networks - ICANN 2010: 20th International

This 3 quantity set LNCS 6352, LNCS 6353, and LNCS 6354 constitutes the refereed lawsuits of the twentieth overseas convention on man made Neural Networks, ICANN 2010, held in Thessaloniki, Greece, in September 2010. The 102 revised complete papers, sixty eight brief papers and 29 posters provided have been rigorously reviewed and chosen from 241 submissions.

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Additional resources for Artificial Neural Networks - ICANN 2010: 20th International Conference, Thessaloniki, Greece, Septmeber 15-18, 2010, Proceedings, Part II

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It can be observed that the degree of reduction achieved by the method depends heavily on the SVM parameters, the best results being obtained when both of them have large values. Note that C and σ are normally selected through a cross–validation procedure, and so their values will depend on the problem at hand. So, AccSO might be able to provide larger improvements in performance depending on the dataset. On the other hand, note that for most of the parameter space either a notable reduction or no reduction at all is obtained.

IEEE Trans. Sign. Proc. 52(8) (2004) 6. : On line classification using kernels and projection based adaptive algorithm. IEEE Trans. Signal Process. 56(7), 2781–2797 (2008) 7. : Adaptive constrained learning in reproducing kernel hilbert spaces: The robust beamforming case. IEEE Trans. Signal Process. 57(12), 4744–4764 (2009) 8. : Iterative kernel principal component analysis for image modeling. IEEE Trans. Pattern Anal. Mach. Intell. 27(9), 1351–1366 (2005) 9. : Adaptive kernel-based image denoising employing semi-parametric regularization: Image Processing.

Final results are in the simplest case obtained using simple voting, but linear discrimination or any other machine approach may be used in the extended space of new features. New feature is added as a hidden node in a constructive network only if it increases the margin of classification, measured by the increase of the aggregated activity of nodes that agree with the final decision. Calculating margin more weight is put on vectors that are close to the decision threshold than on those classified with high confidence.

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Artificial Neural Networks - ICANN 2010: 20th International Conference, Thessaloniki, Greece, Septmeber 15-18, 2010, Proceedings, Part II by Konstantinos Diamantaras, Wlodek Duch, Lazaros S. Iliadis


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