By Ignacio Rojas, Gonzalo Joya, Joan Cabestany
This two-volume set LNCS 7902 and 7903 constitutes the refereed complaints of the twelfth foreign Work-Conference on man made 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 number of submissions for presentation in volumes. The papers discover sections on mathematical and theoretical equipment in computational intelligence, neurocomputational formulations, studying and edition emulation of cognitive capabilities, bio-inspired platforms and neuro-engineering, complex issues in computational intelligence and functions
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We want to take this chance to thank all of these individ uals who helped us gather this article, together with the folk of Lockheed Sanders and Nestor, Inc. , whose encouragement and help have been tremendously preferred. additionally, we want to thank the participants of the Lab oratory for Engineering Man-Machine structures (LEMS) and the heart for Neural technological know-how at Brown college for his or her widespread and worthwhile discussions on a couple of themes mentioned during this textual content.
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Extra info for Advances in Computational Intelligence: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Proceedings, Part 1
I would like to propose to you that we should begin to imagine these changes; that we should begin to study them and that, while waiting for our results, we should begin to take control of the unknown experiment in which we are currently participating. 5 Anthropology-Based Computing I am a great fan of the field of Artificial Neural Networks. I was introduced to it through Science Fiction and I have spent the bulk of my life with a layman’s interest in the process of digitally emulating the manner in which information is stored and processed in the human brain.
IWANN 2013, Part I, LNCS 7902, pp. 17–35, 2013. c Springer-Verlag Berlin Heidelberg 2013 18 A. Lendasse et al. machine learning techniques have very large computational times. The main idea lies in the random initialization of the weights of a Single Hidden Layer Feedfoward Neural Network (SLFN), instead of the traditional —much more time-consuming— learning of these weights through back-propagation , for example. In addition to its speed, which takes the computational time down by several orders of magnitude, the ELM is usually capable to compare with state of the art machine learning algorithms in terms of performance .
If four seconds seems like a long time to you, I assure you that I feel the same way. Fortunately, or perhaps out of simple necessity, if a pattern changes or is over-ridden by a different pattern, we respond to that instantly. It seems to me that all of these weaknesses in our sensory systems, all of the false data we accept, must be a threat to our survival. 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.