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Adaptive and Natural Computing Algorithms: 9th International by Adrian Horzyk (auth.), Mikko Kolehmainen, Pekka Toivanen,

By Adrian Horzyk (auth.), Mikko Kolehmainen, Pekka Toivanen, Bartlomiej Beliczynski (eds.)

This e-book constitutes the completely refereed post-proceedings of the ninth overseas convention on Adaptive and traditional Computing Algorithms, ICANNGA 2009, held in Kuopio, Finland, in April 2009.

The sixty three revised complete papers offered have been rigorously reviewed and chosen from a complete of 112 submissions. The papers are equipped in topical sections on impartial networks, evolutionary computation, studying, gentle computing, bioinformatics in addition to applications.

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Extra info for Adaptive and Natural Computing Algorithms: 9th International Conference, ICANNGA 2009, Kuopio, Finland, April 23-25, 2009, Revised Selected Papers

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Maximum consistency of incomplete data via non–invasive imputation. Artificial Intelligence Review 19 (2003) 6. : Load Prediction Using Combination of Neural Networks and Simple Strategies. Frontiers in Artificial Intelligence and Applications 173, 106–113 (2008) 7. : Demand Prediction with Multi-Stage Neural Processing. In: Advances in Natural Computation and Data Mining, pp. 131–141. Xidian University Press, China (2006) 8. : Evaluation of some popular imputation algorithms. In: Proceedings of the Survey Research Methods Section.

Neural net approximation. In: Narendra, K. ) Proc. 7th Yale Workshop on Adaptive and Learning Systems, pp. 69–72. Yale University Press (1992) 13. : Hinging hyperplanes for regression, classification and function approximation. IEEE Transactions on Information Theory 39, 999–1013 (1993) 14. : Rate of approximation results motivated by robust neural network learning. In: Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory, pp. 303–309. The Association for Computing Machinery, New York (1993) 15.

Remarques sur un r´esultat non publi´e de B. Maurey. In: ´ S´eminaire d’Analyse Fonctionnelle 1980-1981, Ecole Polytechnique, Centre de Math´ematiques, Palaiseau, France, vol. I(12) 17. : Approximation and learning of convex superpositions. J. of Computer and System Sciences 55, 161–170 (1997) 18. : Dimension-independent rates of approximation by neural networks. , K´ arn´ y, M. ) Computer-Intensive Methods in Control and Signal Processing. The Curse of Dimensionality, pp. 261–270. Birkh¨ auser, Basel (1997) 19.

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