Sentence examples for samples the training from inspiring English sources

In the case of a nonlinear classification of samples, the training vectors x i are mapped into a higher (maybe infinite) dimensional space by the function φ, w is the vector of hyperplane coefficients (orientation), b is a bias term.

Unsupervised hierarchical cluster analysis of half of these samples (the training set) using 460 orthologous genes corresponding to our mouse PCT-TC signature revealed two main clusters (Groups A and B), and each cluster was composed of three sub-clusters (A1, A2, A3 and B1, B2, B3, Fig. 2A).

For the IPF biopsy samples, the training dataset is summarized in an unsupervised hierarchical cluster.

One of the sub-sets (the test set) was omitted, and mean pathprints were calculated for each tissue from the remaining samples (the training set).

Thus, a constant shift removing the mean deviation (md) on a subset of samples (the training set) reduces the mean square deviation (msd) on another subset of samples (the validation set).

Gresham et al. (2006) suggest that, when using SNPscanner to genotype new samples, the training data provided by the authors are sufficient, i.e., that it is not necessary to retrain the model on locally produced training data.

The 2516 samples were grouped into training dataset and testing dataset at the ratio of 4 : 1; that is, we used 80% of the samples as the training samples, because sufficient samples were needed to train the predictor.

In each run, we randomly selected two third of the samples as the training samples and the rest as the testing samples.

The final classifier obtained by RVM depends only on fewer samples in the training samples, i.e., RVs.

Let us suppose that not all nodes have access to all the samples, so the training samples accessible from node j is the subset ({S_{n}^{j}}).

The final classifier obtained by SVM depends only on the "borderline" samples in the training samples, i.e., support vectors (SVs).