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Adaptive and Natural Computing Algorithms: 11th by Toshinori Deguchi, Junya Fukuta, Naohiro Ishii (auth.), PDF

By Toshinori Deguchi, Junya Fukuta, Naohiro Ishii (auth.), Marco Tomassini, Alberto Antonioni, Fabio Daolio, Pierre Buesser (eds.)

ISBN-10: 3642372120

ISBN-13: 9783642372124

ISBN-10: 3642372139

ISBN-13: 9783642372131

The ebook constitutes the refereed complaints of the eleventh overseas convention on Adaptive and common Computing Algorithms, ICANNGA 2013, held in Lausanne, Switzerland, in April 2013.
The fifty one revised complete papers provided have been conscientiously reviewed and chosen from a complete of ninety one submissions. The papers are prepared in topical sections on neural networks, evolutionary computation, tender computing, bioinformatics and computational biology, complicated computing, and applications.

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Read or Download Adaptive and Natural Computing Algorithms: 11th International Conference, ICANNGA 2013, Lausanne, Switzerland, April 4-6, 2013. Proceedings PDF

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Extra info for Adaptive and Natural Computing Algorithms: 11th International Conference, ICANNGA 2013, Lausanne, Switzerland, April 4-6, 2013. Proceedings

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Of Approximation Theory 99, 68–94 (1999) 13. : A fast learning algorithm for deep belief nets. Neural Computation (2006) 14. : Learning deep architectures for AI. Foundations and Trends in Machine Learning 2, 1–127 (2009) 15. : Learning Highly Non-separable Boolean Functions Using Constructive Feedforward Neural Network. P. ) ICANN 2007, Part I. LNCS, vol. 4668, pp. 180–189. Springer, Heidelberg (2007) 16. : The curse of highly variable functions for local kernel machines. In: Advances in Neural Information Processing Systems, vol.

In: Proceedings of IEEE Workshop of Nonlinear Image Processing, pp. 70–73 (1995) 7. : Some Comparisons of Networks with Radial and Kernel Units. , Palm, G. ) ICANN 2012, Part II. LNCS, vol. 7553, pp. 17–24. Springer, Heidelberg (2012) 8. : Finite mapping by neural networks and truth functions. Mathematical Scientist 17, 69–77 (1992) 9. : Interpolation of scattered data: Distance matrices and conditionally positive definite functions. Constructive Approximation 2, 11–22 (1986) 10. : Dependence of computational models on input dimension: Tractability of approximation and optimization tasks.

Scaling appeared to preserve stability of the network during these extremely long runs. 2 h) in the absence of synaptic homeostasis (Fig. 4). STDP was turned off during the final 800 s in order to test recall. We found that any training signal frequency eventually pushed the network into a state of excessive firing. This occurred even when E→I STDP balancing was added (not shown). 5 (a) E activity during training (b) Raster plot of 500 ms showing highfrequency network activity after 2 h Fig. 4. 5 (b) Scale factors of E cells Fig.

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Adaptive and Natural Computing Algorithms: 11th International Conference, ICANNGA 2013, Lausanne, Switzerland, April 4-6, 2013. Proceedings by Toshinori Deguchi, Junya Fukuta, Naohiro Ishii (auth.), Marco Tomassini, Alberto Antonioni, Fabio Daolio, Pierre Buesser (eds.)


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