By Gürsel Serpen PhD (auth.), Okyay Kaynak, Ethem Alpaydin, Erkki Oja, Lei Xu (eds.)
This ebook constitutes the refereed complaints of the joint foreign convention on man made Neural Networks and foreign convention on Neural info Processing, ICANN/ICONIP 2003, held in Istanbul, Turkey, in June 2003.
The 138 revised complete papers have been conscientiously reviewed and chosen from 346 submissions. The papers are prepared in topical sections on studying algorithms, aid vector computer and kernel tools, statistical facts research, development attractiveness, imaginative and prescient, speech acceptance, robotics and regulate, sign processing, time-series prediction, clever platforms, neural community undefined, cognitive technology, computational neuroscience, context conscious platforms, complex-valued neural networks, emotion reputation, and purposes in bioinformatics.
Read Online or Download Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003: Joint International Conference ICANN/ICONIP 2003 Istanbul, Turkey, June 26–29, 2003 Proceedings PDF
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Additional info for Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003: Joint International Conference ICANN/ICONIP 2003 Istanbul, Turkey, June 26–29, 2003 Proceedings
10, pp. 252–276, 1998. 15. Y. Matsuyama, N. Katsumata, Y. Suzuki and S. Imahara, The α-ICA algorithm, Proc. Int. Workshop on Independent Component Analysis, pp. 297-302, 2000. 16. Y. Matsuyama and S. Imahara, The α-ICA algorithm and brain map distillation from fMRI images, Proc. Int. Conf. on Neural Information Processing, vol. 2, pp. 708–713, 2000. 17. Y. Matsuyama and R. Kawamura, Supervised map ICA: Applications to brain functional MRI, Proc. Int. Conf. on Neural Information Processing, vol.
Winner leaf for ei in the SGNT: nwin . The SGNT algorithm is a hierarchical clustering algorithm. The pseudo C code of the SGNT algorithm is given in Figure 1. In Figure 1, several sub procedures are used. Table 1 shows the sub procedures of the SGNT algorithm and their speciﬁcations. In order to decide the winner leaf nwin in the sub procedure choose(e i,n 1), the competitive learning is used. This sub procedure is recursively used from the root to the leaves of the SGNT. If an nj includes the nwin as its descendant in the SGNT, the weight wjk (k = 1, 2, .
Conf. on Neural Networks. San Diego, CA. (1987) 609–618 7. Werbos, P. : Generalization of Backpropagation with Application to A Recurrent Gas Market Model. Neural Networks. 1 (1988) 234–242 8. , Livingston, D. : An Adaptive Constraint Satisfaction Network. Proc. ASILOMAR Conf. Signals, Systems and Circuits. Monterey, California. (1990) 163–167 9. : Simultaneous Recurrent Neural Network Addressing the Scaling Problem in Static Optimization. Neural Systems. 11 (2001) 477–487 10. OSC – Ohio Supercomputer Center, Columbus, Ohio, USA, 2002.
Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003: Joint International Conference ICANN/ICONIP 2003 Istanbul, Turkey, June 26–29, 2003 Proceedings by Gürsel Serpen PhD (auth.), Okyay Kaynak, Ethem Alpaydin, Erkki Oja, Lei Xu (eds.)