By Konstantinos Diamantaras, Wlodek Duch, Lazaros S. Iliadis
This 3 quantity set LNCS 6352, LNCS 6353, and LNCS 6354 constitutes the refereed complaints 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 awarded have been rigorously reviewed and chosen from 241 submissions. the second one quantity is split in topical sections on Kernel algorithms – help vector machines, wisdom engineering and selection making, recurrent ANN, reinforcement studying, robotics, self organizing ANN, adaptive algorithms – platforms, and optimization.
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Additional info for Artificial Neural Networks - ICANN 2010: 20th International Conference, Thessaloniki, Greece, Septmeber 15-18, 2010, Proceedings, Part II
Kernel Adaptive Filtering. Wiley, Chichester (2010) 13. : Zur formalen theorie der functionen von mehr complexen ver¨ anderlichen. Math. Ann. 97, 357–375 (1927) 14. : Widely linear estimation with complex data. IEEE Trans. Signal Process. 43(8), 2030–2033 (1995) 15. : Complex-valued adaptive signal processing. , Haykin, S. ) Adaptive Signal Processing: Next Generation Solutions, Hoboken, NJ. Wiley, Chichester (2010) 20 P. Bouboulis and S. Theodoridis 16. : Complex ICA using nonlinear functions.
In this work we have numerically shown how there is a further speed gain in second order SMO if its standard descent directions are replaced, when appropriate, by the combination of the successive descent steps between two appearances of a repeated index pair. We thus arrive at a simple procedure to accelerate second order SMO training. The question that remains is the reason for this faster convergence. While we do not have a full answer at this moment, there are some facts that may partially explain why this is so.
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.