In the last decade, ensemble learning has become a prolific discipline in pattern recognition, based on the assumption that the combination of the output of several models obtains better results than the output of any individual model.
This information, coupled with different classification methods (Partial Least Square Discrimination Analysis "PLS-DA" and Support Vectors Machines "SVM"), was able to discriminate the wine vinegar category within each PDO, for which SVM models obtained better results (>92% of classification).
The main empirical results show that the proposed copula MFV model obtains better hedging effectiveness than the copula GARCH-type models in general.
The experimental results show that our model obtains better classification accuracy on all 5 data sets, even on a small-sized training data.
We observed that the MCGP model obtained better solutions as compared to the GP model.
When the number of genes per leaf increased, so the depth of the trees was decreased, the ts-RF model obtained better results compared to other models on most cases, as shown in Table 7.
We will continue to compare spectral fits to XMM and Chandra observations and refine the models to obtain better comparisons between instruments.
We then adjusted the location and orientation of the initial fault models to obtain better alignment with the hypocenter distribution of their aftershocks in Figs. 2 and 3. We manually fit the data with our own eyes to estimate more robust and reliable fault geometry than that by fitting them with regression analysis because of the existence of events occurring off the main shock faults (Fig. 3).
The first approach is descriptive and employs hierarchical Poisson models to obtain better fits than the homogeneous Poisson distribution used earlier [ 2].
We compared stage-wise approaches to analyse a real dataset of a multi-environment trial (MET) in rye, which was connected between years only through one check, and used different spatial models to obtain better estimates, and thus, improved predictive abilities for genomic prediction.
