For SVM classifiers, we conducted 5-fold cross-validation using the training data for each round to select parameter pair c and gamma, and then a classifier trained using the selected parameter pair was evaluated using the test data (parameter pair was selected from c = 1, 4, 16, 64 and gamma = 0.001, 0.01, 0.1, 1).
To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the framework of CBMeMBer filters, so that the model with unknown maneuvering parameter can be modified adaptively by using the selected parameter particles.
Finally, a least square support vector machine (LSSVM) model was generated using the selected parameters to represent/predict the electrical conductivity of ILs.
Then, FeAl samples were produced using various selected parameters according to the previously established processing map.
The network was constructed by using the selected parameters and alpha-galactosidase production as input and output neurons.
In this experiment, the pool was sequenced to 40× read depth, which using the selected parameters gave 83% sensitivity, 9% false positives, and 81% correct frequency estimates.
Based on previous studies (Park et al. 2015; Yim et al. 2014), this study performed an experimental analysis using selected parameters of mechanical strength (compressive and tensile strength) and measured nonlinearity parameters (HNP and nonlinearity parameter); the initial and post-fire-damage material properties are presented in Table 2.
We then used the selected parameters to train a model on the full training dataset.
Using selected parameters, HEA claddings mostly free of defects were successfully manufactured with very minimal dilution.
We then used the selected parameters in the training and test phase by applying 5-fold cross validation.
