A more fundamental issue is that a prediction model is developed, based on training of the available dataset, to predict new sample(s), classifying new patients as high or low risk based on the available data.
In our experiments, we set the values of both α and the coefficients w m based on training images from our dataset, as explained in Section 3. A path γ between any two nodes v, v′ in V is composed of a sequence of neighboring lattice locations: (4) γ (v, v ′ ) = (v = v 1, v 2, …, v k = v ′ ), subject to the constraint that (v i, v i+1) ∈ E, for i ∈ [1, k – 1].
The network parameters are obtained through training by a greedy layer-wise training method [32] based on a training dataset containing observed speech signals (with one source speech signal placed at a different direction with respect to the microphones).
Since we did not know the ratio of positive to negative samples in their training dataset, we established a prediction model based on a training dataset in which the negative samples were three times the positive ones and only reported the sensitivity of the prediction model.
- based on training needs analysis.
The PCA was either based on the training dataset alone or based on all lines, regardless of which lines were included in the training dataset.
As for the 88 drugs in the test dataset Ste, the predictor was modeled only based on the training dataset Str without involving Ste.
Leave-one-out cross validation (LOOCV) was used to evaluate the reliability of the LDA model based on the training dataset, followed by challenging the model with the test dataset.
Based on our training dataset, the false positive rate of this set can be estimated to be 28% (see Supporting online information [SI] file [Discussion S1] for details).
