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(A3) Keeping the total available x constant and varying the number of individuals with GBS data in the training set (and SNP array data for the same markers in the prediction set), accuracies of EBV were generally maximized by using large training sets that comprised individuals with a low x, rather than by generating small training sets that comprised individuals with a high x.
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May using larger training data set provide better performance for larger number of mixtures?
In addition, an iterative symbol detector was presented to mitigate the superimposed training effects on information sequence recovery, thereby offering an alternative to enhance the channel estimation performance by using a large training power while without sacrificing SER performance degradation.
The final building behavior lies in between these cases, and is predicted by producing a linear combination of the models through a single model-parameter α that has been calibrated by using a large training database and an adaptive neural network with a fuzzy inference system, ANFIS.
Compared with conventional ST methods [9, 11, 16 22, 24], the iterative scheme offers an alternative to enhance the channel estimation performance by using a large training power while without sacrificing SER performance degradation.
In absolute terms, however, the bicycle-train integrations scenarios have stronger effects on train passengers using large and commuter train stations (NS-types 1 3).
Avoid using large images.
Depending upon the availability of more crystal structures with swapped conformation, the method could be improved by re-training the model using larger datasets.
The authors used relatively large training set as well.
During the estimation process, the ISI is avoided by letting the pulse repetition interval be larger than, and the MSI is avoided by using orthogonal training symbols.
The BP-ANN was trained by using a large (>1,000 cases) heterogeneous data set containing masses and microcalcifications.
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