Sentence examples for multiple training samples from inspiring English sources

All the algorithms which need multiple training samples have a possibility encountering this effect.

When we examine the cross-validation folds, Orkney has both a higher proportion of testing samples with at least one relative in the training data and a higher proportion of testing samples that are related to multiple training samples compared with Croatia (Supplementary Material, Fig. S3).

In our cases here, it is certainly unfair to compare the CRB for an ML estimator based on one training block sample with the estimator MSE using multiple training block samples.

In fact, one can also go to great lengths to apply the ML algorithm to perform the usual joint frequency tracking and channel estimation but using multiple training block samples.

INRIA person dataset [24] is considered to be one of the most comprehensive and flexible datasets containing 4754 images divided into testing and training samples under multiple scenarios of positive and negative samples.

This is a benchmark analysis to evaluate the feasibility of re-using pre-existing datasets as training samples for multiple-class prediction models.

Fortunately, by using RT-qPCR validation in a large number of individual serum samples arranged in multiple training and validation sets, we successfully identified the five-miRNA panel and additional bind trial test further confirmed our findings.

This was demonstrated by the IPRE algorithm as we incorporated multiple training datasets with a very large number of samples across multiple microarray platforms.

A random forest (RF) classifier is an ensemble classifier that produces multiple decision trees, using a randomly selected subset of training samples and variables.

Here, we selected two-thirds of the total number of samples as the training sample set, and obtained the support vectors and the construct of the SVM model through multiple training iterations.

They modeled the distribution of multiple action classes as a mixture subspace model and represented the test sample with linear approximation of all training samples.