Sentence examples for training regression from inspiring English sources
Where sufficient data for training regression models are available, quantitative prediction can also be attempted [12, 14, 28, 40].
Before training regression models using the custom PBM data, we filtered out any sequence that contained potential TF binding sites in the regions flanking the CACGTG core, to ensure that each sequence contains one and only one TF binding site.
After training regression models for the predictable gene set using 2/3 of the TCGA data, the average accuracy (ranked correlation of true and predicted values) in the 1/3 testing partition and four independent populations is above 0.65 and approaches 0.8 for conservative parameter sets.
Secondly, it obtains potential energy saving effect of concurrent kernels by using the off-line trained regression model.
The authors then trained regression models using a variety of algorithms (with SVMs having the best performance) and a variety of extracted features.
Figure 2 shows the two samples of the test and the train regression charts for the proposed ANN. Figure 2 displays the good quality of the trained network prediction.
The trained regression algorithms and the approach based on the Nicholson-Shain equation were then used to calculate the diffusion coefficient for each of the 80 experimental voltammetric curves.
For each cross validation experiment, four folds (plus 422 intermediate samples and 2004 negative samples) are used as the training set and then the trained Regression model is used to detect the complexes in the DIP PIN.
Next, we derive quantitative features from the sequence content of the genomic regions flanking the CACGTG E-box core, and we use them to train regression models that can predict the PBM signal intensity (i.e. the in vitro TF DNA binding specificity).
The first stage of forecasting is training a regression model considering load variation, which is proposed as a quantile regression neural network (QRNN) in this paper, generating probabilistic results in the form of quantiles.
Input: set of documents D, trained logistic regression models P, trained LDA model LDA n Output: set of ordered criteria S★.
