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We tried to improve the regression performance by also incorporating more elaborate models and additional data.
For measuring the regression performance of the five compared association models, we use a 5-fold cross-validation strategy by computing the Pearson's correlation coefficient (CORR) and the root mean square error (RMSE) between the predicted and the actual SNP values, which are reported in Figure 1.
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The regression performances vary with different selection of four important parameters: The kernel function f The penalty parameter c The kernel parameter g and The degree of the kernel function d.
The regression performances vary with different selection of four important parameters: (1) the kernel function (2) penalty parameter c (3) kernel parameter g and (4) degree of the kernel function d.
The underlying reason is the low regression performance for the valence dimension.
Another way is that of searching the values of the parameters via some intelligent methods of optimization of the SVR regression performance: for doing this efficiently, one must avoid problems like divergence, slow convergence, local optima, etc.
The clearly improved regression performance in the prediction and highly suggestive imaging markers selected by our new method have validated its effectiveness.
To verify the classification and regression performance of DrELM, we conduct the experiments on both synthetic and real-world data sets.
A comparative study is carried out to compare support vector machines regression performance with the one of the neural networks, nonlinear regression, and different empirical correlation techniques.
Stratified Cox-proportional hazards regression performance of the seven-gene MDAhet classifier, lymph node status (LN) and chemotherapy (CT) across oestrogen receptor-negative training and test sets, with strata defined by cohorts.
Adding more other configuration metrics does not improve the regression model performance more than 1%.
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CEO of Professional Science Editing for Scientists @ prosciediting.com