By Dominic Palmer-Brown, Miao Kang (auth.), Dr. Bernardete Ribeiro, Dr. Rudolf F. Albrecht, Dr. Andrej Dobnikar, Dr. David W. Pearson, Dr. Nigel C. Steele (eds.)

The ICANNGA sequence of meetings has been organised given that 1993 and has a protracted heritage of selling the foundations and knowing of computational intelligence paradigms in the clinical group and is a reference for confirmed staff during this sector. beginning in Innsbruck, in Austria (1993), then to Ales in Prance (1995), Norwich in England (1997), Portoroz in Slovenia (1999), Prague within the Czech Republic (2001) and eventually Roanne, in France (2003), the ICANNGA sequence has demonstrated itself for knowledgeable staff within the box. The sequence has additionally been of worth to younger researchers wishing either to increase their wisdom and adventure and likewise to satisfy the world over popular specialists. The 2005 convention, the 7th within the ICANNGA sequence, will ensue on the collage of Coimbra in Portugal, drawing at the event of earlier occasions, and following an analogous normal version, combining technical classes, together with plenary lectures by way of popular scientists, with tutorials.

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Additional resources for Adaptive and Natural Computing Algorithms: Proceedings of the International Conference in Coimbra, Portugal, 2005

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Recent theoretical results in this field show that for functions of one variable, different values of u and d can produce very different results: fast convergence at the cost of a poor solution, slow convergence with a better solution, or produce a fast move towards a solution but without converging. To speed up backpropagation in a simple manner we propose a batch step adaptation technique for the online backpropagation algorithm based on theoretical results on simple cases. 1 Introduction Speeding up backpropagation has been a constant challenge and several techniques has been applied like using second order information [4].

Bengio Y, Haffner, P. (1998) Gradient-based learning applied to document recognition. Proc. of the IEEE, vol. 86, nr. 11, 1998. [4] Spall, J. , IEEE Trans. Autom. 45, nr. 10: 1839-1853. [5] Silva, F. , Almeida, L. B. (1990),Speeding up backpropagation, Advanced Neural Computers, ed. R. Eckmiller, Elsevier Science Publishers, Amsterdam, ppl51-158. , Hemmen, van J. , (1996), Accelerating Backpropagation through Dynamic Self-Adaptation, Neural Networks, vol. 9, nr. 4: 589-601. , Cruz, P. (to be published), A stochastic approximation algorithm with multiplicative step size adaptation.

Denote by Je = {tf,rf ; / G Le n / } the set of neurons local to e that are associated with the internal edges from L e . Unit £e is connected with all neurons from Je via negative weights except for units re2 (if e2 G / ) , £e, and r e4 (if e4 G / ) whose states may encode a tristrip that traverses edge e by the left turn. Such a situation (for Le C / ) is depicted in Figure 3 where the edges shared by triangles within the tristrip together with associated active neurons re2, £e,re4 are marked.

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Adaptive and Natural Computing Algorithms: Proceedings of by Dominic Palmer-Brown, Miao Kang (auth.), Dr. Bernardete
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