Download Artificial Neural Networks - ICANN 2008: 18th International by Anton Andrejko, Mária Bieliková (auth.), Véra Kůrková, Roman PDF

By Anton Andrejko, Mária Bieliková (auth.), Véra Kůrková, Roman Neruda, Jan Koutník (eds.)

This quantity set LNCS 5163 and LNCS 5164 constitutes the refereed lawsuits of the 18th foreign convention on man made Neural Networks, ICANN 2008, held in Prague Czech Republic, in September 2008.

The 2 hundred revised complete papers offered have been rigorously reviewed and chosen from greater than three hundred submissions. the second one quantity is dedicated to trend acceptance and information research, and embedded structures, computational neuroscience, connectionistic cognitive technological know-how, neuroinformatics and neural dynamics. it additionally comprises papers from designated classes coupling, synchronies, and firing styles: from cognition to ailment, and optimistic neural networks and workshops new traits in self-organization and optimization of man-made neural networks, and adaptive mechanisms of the perception-action cycle.

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Extra info for Artificial Neural Networks - ICANN 2008: 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part II

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G. by cross-validation) and (iii) using the assessment to estimate the relevance of the subset s. Given a learner h trained on dataset of size N , the wrapper approach translates the (learner independent) maximization problem (2) into the (learner dependent) minimization problem arg min s⊆x,|s|≤d Msh = arg min s⊆x,|s|≤d S MMEh (s)dFs (s) (3) where MMEh is the Mean Misclassification Error (MME) of h in the point s. For a zero-one loss function L, since y and y ˆ = h(s) are independent, MMEh (s) = Prob {y = y ˆ|s} and its bias/variance decomposition [11] is: ⎛ ⎛ ⎞⎞ K 1 2 Prob {y = yj |s} ⎠⎠ + MMEh (s) = ⎝1 − ⎝ 2 j=1 K 1 (Prob {y = yj |s} − Prob {ˆ y = yj |s})2 + 2 j=1 ⎛ ⎛ ⎞⎞ K 1⎝ 1 2 + 1−⎝ y = yj |s} ⎠⎠ = (n(s) + b(s) + v(s)) Prob {ˆ 2 2 j=1 + (4) where n(s) = 1 − r(s) is the noise variance term, b(s) is the learner squared bias and v(s) is the learner variance.

16 A. M. Dias, and A. Mota Fig. 7. Block diagram of the test system The transfer function of the plant is presented in 1. 5 (1) The system was simulated using TrueTime, a MATLAB/Simulink based simulator for real-time distributed control systems [1]. 2 Existing Controller The existing controller is of the pole-placement type. It does not take into account the sampling to actuation delay. The controller parameters are constant and computed based on the discrete-time transfer function given by equation 2.

5 Method Evaluation and Conclusions We described a method for comparing instances of ontological concepts based on recursive traversing of instance’s structure. Final similarity is a result of mean aggregation of similarities computed for particular attributes while their type is considered. Introducing similarity computed for individual attributes allows employing semantics from ontology representation. It allowed us to extend similarity enumeration with weights to compute similarity for particular user to be used for personalization purposes.

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