By Ignacio Rojas, Gonzalo Joya, Joan Cabestany
This two-volume set LNCS 7902 and 7903 constitutes the refereed complaints of the twelfth overseas 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 a variety of submissions for presentation in volumes. The papers discover sections on mathematical and theoretical equipment in computational intelligence, neurocomputational formulations, studying and variation emulation of cognitive services, bio-inspired platforms and neuro-engineering, complex themes in computational intelligence and functions
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Extra info for Advances in Computational Intelligence: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Proceedings, Part 1
3) outperforms the Lasso. e. there are many j such that wˆ j = 0. In addition, the control over the sparsity by the parameter λ can be challenging to tune. a. Ridge Regression ). This time, the minimization problem involves a penalty using the square of the regression coeﬃcients ⎡ n ⎤ p ⎢⎢⎢ ⎥⎥⎥ 2 2 ⎢ ˆ +λ wˆ j ⎥⎥⎥⎦ . min ⎢⎢⎣ (yi − xi w) (9) ˆ λ,w i=1 j=1 Thanks to a bias–variance tradeoﬀ, the Tikhonov regularization achieves better prediction performance than the traditional OLS solution. 2, it outperforms the Lasso solution in cases were the variables are correlated.
If the tools by which we sit at a computer and input information are not humancentered, then how can changing a software interface be considered an improvement to Human-Computer Interaction? This question is equally valid the colour of a casing, the size of a monitor, and the speed of a processor. There is another fundamental aspect of the common computer that is as pervasive as the three discussed above, and equally non-human-centered. It will be discussed in depth a little later. 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.
Wˆ n )T the estimated regression weights. with w The solution of Eq. 5 is then obtained by a classical pseudo-inverse (Moore-Penrose ) as −1 ˆ OLS = XT X XT y, w (7) assuming that X is full rank. This way of computing the solution involves matrix inversion (for the computation −1 of the inverse covariance matrix XT X ) which tends to pose numerical problems in practice, since X is sometimes not full rank (there might very well be irrelevant or linear combinations of samples and/ or variables in the data set).