Sentence examples for between training and data from inspiring English sources

The proposed optimal SIT consists of two parts, optimal training sequences deduced with respect to the channel estimate's mean square error (MSE) and optimal power allocation between training and data derived by minimizing the averaged channel capacity lower bound.

The optimal power allocation between training and data at each sensor over orthogonal channels that are subject to path loss, Rayleigh fading, and Gaussian noise is derived in [16].

We consider three resource allocation problems: power allocation between training and data symbols, time/bandwidth allocation to the relay, and power allocation between the source and relay under a total power constraint.

Gao et al. [ 31] found that the advantage of the mixture model over the BLUP was more profound with weak relationships between training and data sets, and the authors argued that the mixture model captured LD between markers and a QTL more efficiently than the BLUP model.

The suboptimal performance of Random Forest is mainly due to the classification scheme of the tree-like structure, which is sensitive to data variations between training and test data like ours.

For our proposed method, which exploits discriminative training methods, the best performing system is different from environment to environment due to the variety of evaluation data or mismatch between training and evaluation data.

The fact that the Pre-diagnosis data was a subset of the full dataset meant that applying the All-data model to Pre-diagnosis observations would have artificially inflated accuracy, due to information leakage between training and test data.

This is because it reduces the dependence between training and test data by fully leveraging the two omics data sets with a large number of observations.

Information leakage between training and test data, despite the best intentions, is very hard to avoid.

A domain adaptation method is adopted to minimize the maximum mean discrepancy between training and testing data.

The maximum a posteriori (MAP) criterion is popularly used for feature compensation (FC) and acoustic model adaptation (MA) to reduce the mismatch between training and testing data sets.