By Marcel A. J. van Gerven, Eric Maris (auth.), Timo Honkela, Włodzisław Duch, Mark Girolami, Samuel Kaski (eds.)
ISBN-10: 3642217370
ISBN-13: 9783642217371
ISBN-10: 3642217389
ISBN-13: 9783642217388
This quantity set (LNCS 6791 and LNCS 6792) constitutes the refereed complaints of the 21th overseas convention on synthetic Neural Networks, ICANN 2011, held in Espoo, Finland, in June 2011. The 106 revised complete or poster papers provided have been rigorously reviewed and chosen from quite a few submissions. ICANN 2011 had uncomplicated tracks: brain-inspired computing and laptop studying examine, with powerful cross-disciplinary interactions and applications.
Read or Download Artificial Neural Networks and Machine Learning – ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part II PDF
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Additional info for Artificial Neural Networks and Machine Learning – ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part II
Example text
Signals of neuronal activity were mapped by continuous wavelet transformation to the temporal-frequency-spatial space. Then the Iterative N-way PLS (INPLS) was applied to extract the predictors (neural electrical features preceding an intention to act). One of the major problems of BCI studies is the variability of neuronal signals, due in particular to the brain plasticity. These changes in neuronal activity require recalibration of BCI systems. The full system recalibration is a time and labor consuming procedure.
G. by a hat. (d) Test images with random masks superimposed. If masks are randomly assigned to test images the alignment of mask and image is considerably worse. Additional training images and segmentation results and a comparison with results for a conventional conditional random field can be found in the supplemental material [13]. (e): Convergence of the segmentation during learning (inferred mask m superimposed on training image before joint training (left most) and after 10, 20, 100 and 1000 epochs of joint training).
Comput. 20(1), 359–392 (1997) 5. : Hierarchical approach for computing spin glass ground states. Phys. Rev. E 64, 56704 (2001) 6. : Transformation of Energy Landscape in the Problem of Binary Minimization. Doklady Mathematics 80(3), 927–931 (2009) 7. : Binary Optimization: Efficient Increasing of Global Minimum Basin of Attraction. Opt. Memory & Neural Net. (Information Optics) 19(2), 110–125 (2010) 8. , Rieger, H. ): Optimization Algorithms in Physics. Wiley-VCH, Berlin (2001) 9. , Rieger, H. ): New Optimization Algorithms in Physics.
Artificial Neural Networks and Machine Learning – ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part II by Marcel A. J. van Gerven, Eric Maris (auth.), Timo Honkela, Włodzisław Duch, Mark Girolami, Samuel Kaski (eds.)
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