Sentence examples for optimizing the prediction from inspiring English sources

Bayesian logistic regression utilizes the best features of both methods by employing the prior information about the success probability and recursively optimizing the prediction parameters to achieve the optimal classification/prediction rather than simple regression coefficient for classifiers [ 42- 44].

The second method consists of optimizing the prediction and update filters as proposed in [20, 38].

The fifth method consists of jointly optimizing the prediction filters by using the proposed weighted ℓ2 minimization technique where the weights κ j + 1 ( o ) are set to 1 α j + 1 ( o ).

The necessary back-ground on convex analysis and proximity operators [52, 53] is given in Appendix A. Now, we recall that our minimization problem (11) aims at optimizing the prediction filters by minimizing the ℓ1-norm of the difference between the current pixel x i,j and its predicted value.

These parameters have been estimated by optimizing the prediction process.

Optimizing the prediction of target sites in regions of more-stably folded local structure (i.e. higher-GC regions) may therefore require more-strict conditions at the secondary filtering stage.

Finally, despite the SuperLearner procedure, which is intended to optimize the prediction, our predictive performance was in fact limited, with an AUROC of 0.760 955% CI 0.694, 0.826) for the SuperLearner weighted algorithm.

A joint optimization method can therefore be proposed which iteratively optimizes the prediction filters p j ( H H ), p j ( H L ), and p j ( L H ). While the optimization of the prediction filters p j ( H L ) and p j ( L H ) is simple, the optimization of the prediction filter p j ( H H ) is less obvious.

We also propose to jointly optimize the prediction filters by using an algorithm that alternates between filter optimization and weight computation.

Related to this fact, we propose to jointly optimize the prediction filters by using an algorithm that alternates between the optimization of the filters and the computation of the weights.

This algorithm first predicts the values, checks for the error and then tries to optimize the prediction.