By Toshinori Deguchi, Junya Fukuta, Naohiro Ishii (auth.), Marco Tomassini, Alberto Antonioni, Fabio Daolio, Pierre Buesser (eds.)
The booklet constitutes the refereed lawsuits of the eleventh foreign convention on Adaptive and average 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 equipped in topical sections on neural networks, evolutionary computation, tender computing, bioinformatics and computational biology, complicated computing, and applications.
Read Online or Download Adaptive and Natural Computing Algorithms: 11th International Conference, ICANNGA 2013, Lausanne, Switzerland, April 4-6, 2013. Proceedings PDF
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Additional resources for Adaptive and Natural Computing Algorithms: 11th International Conference, ICANNGA 2013, Lausanne, Switzerland, April 4-6, 2013. Proceedings
Of Control 2, 237–254 (1965) 8. : Computational Neuroscience. Science, New Series 241(4871), 1299–1306 (1988) 9. : Visual Motion Analysis by a Neural Network. Neural Information Processing 11(4-6), 63–73 (2007) 10. : Marginalization in Neural Circuits with Divisive Normalization. Journal of Neuroscience 31(43), 15310–15319 (2011) 11. : Correlation Computations for Movement Detection in Neural Networks. C. ) KES 2004, Part II. LNCS (LNAI), vol. 3214, pp. 124–130. Springer, Heidelberg (2004) 12. : Neural Computations by Asymmetric Networks with Nonlinearities.
Threshold circuits of bounded depth. In: Proc. 28th Annual Symposium on Foundations of Computer Science, pp. 99–110. IEEE (1987) 28. : Threshold circuits of bounded depth. J. pl Abstract. We propose an asymmetric version of the Self-Organizing Map (SOM) capable to properly visualize datasets consisting of time series. The goal is achieved by introducing an asymmetric coeﬃcient making the asymmetric SOM capable to handle time series. S. Stock Market Dataset verify and conﬁrm the eﬀectiveness of the proposed asymmetric SOM extension.
69–72. Yale University Press (1992) 23. : Complexity estimates based on integral transforms induced by computational units. Neural Networks 33, 160–167 (2012) 24. : Representations and rates of approximation of real–valued Boolean functions by neural networks. Neural Networks 11, 651–659 (1998) 25. : Big omicron and big omega and big theta. SIGACT News 8(2), 18–24 (1976) 26. : Comparison of worst-case errors in linear and neural network approximation. IEEE Trans. on Information Theory 48, 264–275 (2002) 27.