Adaptive and Natural Computing Algorithms: 8th International by Sarunas Raudys (auth.), Bartlomiej Beliczynski, Andrzej

By Sarunas Raudys (auth.), Bartlomiej Beliczynski, Andrzej Dzielinski, Marcin Iwanowski, Bernardete Ribeiro (eds.)

The quantity set LNCS 4431 and LNCS 4432 constitutes the refereed complaints of the eighth overseas convention on Adaptive and typical Computing Algorithms, ICANNGA 2007, held in Warsaw, Poland, in April 2007.

The 178 revised complete papers offered have been conscientiously reviewed and chosen from a complete of 474 submissions. The ninety four papers of the 1st quantity are geared up in topical sections on evolutionary computation, genetic algorithms, particle swarm optimization, studying, optimization and video games, fuzzy and tough platforms, simply as class and clustering. the second one quantity includes eighty four contributions regarding neural networks, aid vector machines, biomedical sign and snapshot processing, biometrics, desktop imaginative and prescient, in addition to to manage and robotics.

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Extra resources for Adaptive and Natural Computing Algorithms: 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceedings, Part II

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Barron, A. : Universal approximation bounds for superpositions of a sigmoidal function. IEEE Transactions on Information Theory 39 (1993) 930–945 4. Carlson, B. C. Kainen, V. K˚ urkov´ a, and M. Sanguineti 5. : Approximation error bounds that use VC-bounds. In Proceedings of the International Conference on Neural Networks, Paris (1995) 295-302 6. : Rates of convergence for radial basis functions and neural networks. In Artificial Neural Networks for Speech and Vision, R. J. ), Chapman & Hall, London (1993) 97-113 7.

In this situation it is necessary to obtain a mathematical description of the neural model uncertainty. The solution to this problem can be application of the Outer Bounding Ellipsoid (OBE) algorithm [6]. In the paper it is shown, how to use the LMS and OBE algorithms to the estimation of the GMDH network parameters in the form of the admissible parameter set called also a parameters uncertainty. This result allows to define the model uncertainty in the form of the confidence interval of the model output.

In the second phase (epochs 101-200) the classifier alone continued to adapt its weights in the first processing variant, while in the second one the adaptation of the orthogonal network was also turned on. The weights of the MLP part were initialized with random values from the range [−1, 1] in both variants. 2 33 Tests Results and Analysis The classification results for both processing variants are presented in Tables 1, 2 respectively. e. for every number of features K, and the averaged values are displayed.

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