By John N. A. Brown (auth.), Ignacio Rojas, Gonzalo Joya, Joan Gabestany (eds.)
This two-volume set LNCS 7902 and 7903 constitutes the refereed complaints of the twelfth foreign Work-Conference on synthetic Neural Networks, IWANN 2013, held in Puerto de l. a. 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 variation emulation of cognitive features, bio-inspired structures and neuro-engineering, complicated subject matters in computational intelligence and applications
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Additional resources for Advances in Computational Intelligence: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013, Proceedings, Part I
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If our brains learn to think while exposed to a particular home environment, wouldn’t we naturally develop an understanding of the world, of how things should and must be, based on that source? If the familiar gives It’s as Easy as ABC 9 us comfort, couldn’t some of that comfort come from the fact that a familiar environment makes it easier for the unusual (and possibly dangerous) to stand out. To see how this might affect (and might be affected by) the ways in which we take in information, let’s go way back in time again, twenty-million years further back than before.
If this seems to be so, then the logic of evolutionary forces tells us that some compensatory force must exist in our natural environment, in our culture or in our behaviour. I believe that compensatory force is teamwork. We are social animals and we watch each other’s backs. While I am immersed in one sensory stream, you are immersed in another and, if all goes well, the two streams will not dry up within four seconds of each other. The more of us who participate, the better our chance of detecting an important change will be.
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).