We analyzed the data in each ROI by testing the performance of a linear support vector classifier trained to predict near versus far depth based on voxel activation patterns evoked when participants viewed targets in the D, M, DM, and D-M conditions.
This result is comparable to the performance achieved by a non-linear support vector machine using state-of-the-art descriptors.
On the remaining three data sets, the best encoding was more than 5% worse than the results of the best encoding published by Sutherland et al. On the Ames toxicity data set, the implementation of the extended connectivity fingerprint achieved an AUC ROC performance of 0.87, which is comparable to the performance by a non-linear support vector machine trained on state-of-the-art descriptors.
In our experiment, we apply two important classes of methods, namely linear large margin methods (represented by the linear support vector machine, SVM) and ensembles of decision trees (Random Forests, RF).
First, relevant information was extracted from each biofluid dataset by use of linear support vector machine recursive feature elimination.
The produced ANN results for years 2005 2008 were compared with the results produced by a linear regression method, a support vector machine method and with real energy consumption records showing a great accuracy.
Statistically, we observed that there were no significant differences for different puncture sites (supported by a linear mixed model analysis of a total of dozens of needle insertions) so the low variability of our data supports the feasibility to use tissue optics as a discrimination tool for epidural needle placement.
The results were supported by a linear relation with an r2 of 0.89 and a low mean square deviation (MSD = 0.24) between the measured and the simulated SOC.
The paper deals with the finite element method (FEM) solution of the problem with loads moving uniformly along an infinite Euler beam supported by a linear elastic Kelvin foundation with linear viscous damping.
This system consists of a plunging and pitching rigid airfoil supported by a linear spring in the plunge degree of freedom and a nonlinear spring which includes the simulated stoppers at high angles in the pitch degree of freedom.
