By John N. A. Brown (auth.), Ignacio Rojas, Gonzalo Joya, Joan Gabestany (eds.)
This two-volume set LNCS 7902 and 7903 constitutes the refereed court cases 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 conscientiously reviewed and chosen from various 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 platforms and neuro-engineering, complicated subject matters in computational intelligence and applications
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Additional info 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
Generally, the computation of the solution to Eq. 8 is a quadratic programming problem with linearity constraint which can be intensive. In , a computationally more eﬃcient algorithm is presented as the LARS algorithm (Least Angle Regression), of which the Lasso is a specific instance. LARS actually generalizes both the Lasso and the Forward Stagewise regression strategy (see  for example): the algorithm starts similarly to Forward selection, with all coeﬃcients equal to zero and finds the variable most correlated with the output.
2 Cross-Generational Habit It is also of interest to note that, in all of the years since the mouse appeared, and despite all of the surface re-designs driven by commercial, legal or therapeutic motivations, very few have ever asked just why it is assumed that the best way to interact with a computer is to rub a block of wood on a desk. This, I believe, is due to another aspect of human behavior, our tendency to maintain with unconscious fervor the behaviors of our grandparents. This is what I call Cross-Generational Habit.
Section 2 then elaborates on one problem encountered by the original OP-ELM, in the computation of the pruning criterion. The Leave-One-Out criterion is originally used in the OP-ELM for the pruning, which can be a computationally costly choice. Thanks to the use of a closed form formula (Allen’s PRESS statistic ), its computation is nevertheless very fast, but raises numerical problems which possibly “disturb” the pruning strategy. This proposed solution to this situation is by the use of L2 regularization in the OPELM.
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 by John N. A. Brown (auth.), Ignacio Rojas, Gonzalo Joya, Joan Gabestany (eds.)