By Ignacio Rojas, Gonzalo Joya, Joan Cabestany
This two-volume set LNCS 7902 and 7903 constitutes the refereed lawsuits of the twelfth overseas Work-Conference on man made Neural Networks, IWANN 2013, held in Puerto de los angeles Cruz, Tenerife, Spain, in June 2013. The 116 revised papers have been conscientiously 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 services, bio-inspired structures and neuro-engineering, complex themes in computational intelligence and purposes
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Extra resources for Advances in Computational Intelligence: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Proceedings, Part 1
This might offer an explanation of our persistent self-delusion and overconfidence. But how would this compensation function in environments where people are expected to work alone? What effect would it have if we were suddenly entrusted to work with machines, in a machine-like manner that depends on our constant awareness of minute details, rather than with colleagues who would be helping us to stay aware of these details through generalised pattern recognition and cooperation? When this happens with the advance of personal technology, it seems to me that we have to choose between two common reactions.
That’s another topic we’ll get back to later. The question I want to ask you about ubiquitous computing, as illustrated by smartphones, is this. If alarms and alerts annoy you when they come from a smartphone at a nearby workstation, park bench or seat in a movie theatre, how much more would they annoy you if you were living inside the phone? The smart environment of the near future will surround us with computerised recommender systems, ambient information systems and distributed interfaces and displays.
ATn , ai ∈ R p . Also A can be referred by A = ai, j 1≤ j≤p A are used for matrices and low-case boldface b for vectors. 1 General Case For the general setup, assume a single-output regression problem of the form y = Xw + ε, with X = xT1 , . . , xTn T (4) the inputs of the problem (data set), y = (y1 , . . , yn )T the actual T output, w = w1 , . . , w p the regression weights and ε = (ε1 , . . , εn )T the residuals. a. Gauss-Markov solution) is a possible approach to solve this problem. The problem can be formulated as a minimization of the Mean Square Error as ˆ T (y − Xw) ˆ , min (y − Xw) ˆ w or in a non matrix form (5) n (yi − xi w) ˆ 2, min ˆ w (6) i=1 ˆ = (wˆ 1 , .
Advances in Computational Intelligence: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Proceedings, Part 1 by Ignacio Rojas, Gonzalo Joya, Joan Cabestany