Download Advances in Computational Intelligence: 12th International by Ignacio Rojas, Gonzalo Joya, Joan Cabestany PDF

By Ignacio Rojas, Gonzalo Joya, Joan Cabestany

This two-volume set LNCS 7902 and 7903 constitutes the refereed court cases of the twelfth foreign Work-Conference on synthetic Neural Networks, IWANN 2013, held in Puerto de los angeles Cruz, Tenerife, Spain, in June 2013. The 116 revised papers have been rigorously reviewed and chosen from a variety of submissions for presentation in volumes. The papers discover sections on mathematical and theoretical tools in computational intelligence, neurocomputational formulations, studying and edition emulation of cognitive capabilities, bio-inspired platforms and neuro-engineering, complex subject matters in computational intelligence and purposes

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Additional resources for Advances in Computational Intelligence: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Proceedings, Part 1

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In [1] is proposed a theorem — on which lies the efficiency of the ELM — stating that with a random initialization of the input weights and biases for the SLFN, and under the condition that the activation function is infinitely differentiable, the hiddenlayer output matrix can be determined and will provide an approximation of the target values as good as wished (nonzero). Under the conditions detailed in [22] — that is, randomly generated hidden nodes weights and bounded non-constant piecewise continuous activation function — the ELM is a universal function approximator [23,24].

Extreme Learning Machine: A Robust Modeling Technique? Yes! Amaury Lendasse1,2,3 , Anton Akusok1 , Olli Simula1 , Francesco Corona1, Mark van Heeswijk1 , Emil Eirola1 , and Yoan Miche1 1 3 Information and Computer Science Department Aalto School of Science and Technology FI-00076 Aalto, Finland 2 IKERBASQUE, Basque Foundation for Science 48011 Bilbao, Spain Computational Intelligence Group, Computer Science Faculty, University of the Basque Country Donostia/San Sebastian, Spain Abstract. In this paper is described the original (basic) Extreme Learning Machine (ELM).

The truth is that every day, each one of us is training to respond, or not to respond, to selected environmental stimuli. We don’t even know we are doing it most of the time. Have you learned to ignore an alarm on your smart phone? Maybe the first alarm that rings in the morning? Have you learned to ignore the train that passes near your window several times a day? We live in an environment full of stimuli we have chosen to ignore. Did you know that there is a blind spot in the middle of the image sent to your brain from each of your eyes?

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