Kernel learning at the first level of inference

Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e.parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense

Impact of videogame playing on glucose metabolism in children with type 1 diabetes.

Phan-Hug F, Thurneysen E, Theintz G, Ruffieux C, Grouzmann E. Impact of videogame playing on glucose metabolism in children with type 1 diabetes. Time spent playing videogames (VG) occupies a continually increasing part of children's leisure time. They can generate an important state of excitation, representing a form of mental and physical stress. This pilot study aimed to assess whether VG influences glycemic balance in children with type 1 diabetes. Twelve children with type 1 diabetes were subjected to two distinct tests at a few weeks interval: (i) a 60-min VG session followed by a 60-min rest period and (ii) a 60-min reading session followed by a 60-min rest period. Heart rate, blood pressure, glycemia, epinephrine (E), norepinephrine (NE), cortisol (F), and growth hormone (GH) were measured at 30 min intervals from -60 to +120 min. Non-parametric Wilcoxon tests for paired data were performed on Δ-values computed from baseline (0 min). Rise in heart rate (p = 0.05) and NE increase (p = 0.03) were shown to be significantly higher during the VG session when compared to the reading session and a significant difference of Δ-glycemic values was measured between the respective rest periods. This pilot study suggests that VG playing could induce a state of excitation sufficient to activate the sympathetic system and alter the course of glycemia. Dietary and insulin dose recommendations may be needed to better control glycemic excursion in children playing VG