Sentence examples for data set dimension from inspiring English sources

A fair agreement between these data can be observed, although the different testing conditions, vehicle type, nationality, data set dimension, and test track type can easily explain the differences among the distributions, even more when considering the marked difference in distribution when classifying different track segments on the basis of the local curvature.

It is worth noting that the data set dimension is independent from the size of the original images.

We show the applicability of graphical models on ICF data for different tasks: Visualization of the dependence structure of the data set, dimension reduction and comparison of subpopulations.

And fourth, the time needed for training an MLP is reduced in ≈ 45%:from ≈ 55 s in the original case to ≈ 30 s with this new data set dimensioning.

And seventh, since the number of measurements used now for designing MLPs is lower than in the original case, the time needed for training an MLP is reduced in ≈ 60%: from ≈ 30 s in the original case to ≈ 12 s with this new data set dimensioning.

Our LLE input data set was dimensioned using the Cartesian coordinates of heavy atoms of TC5b (154 atoms × 3 (x,y,z) = 462 dimensions), and included 2,355 configurations sampled from the 363 K replica to model the unfolded state ensemble, or from the 310 K replica to model the folding ensemble.

This arrangement implies that in a high-dimensional data set, certain dimensions have to be combined.

We assume that we have a training data set X of dimension p × N, where p is the number of dimensions (ie, genes) and N is the number of training samples (ie, tumour samples).

The eigenvectors with the larger eigenvalues are selected to reconstruct a new data set to achieve dimension reduction according to the PCA.

This would suggest that in the current data set a second dimension was not present.

It thus preserves the whole information content of the original data set despite the dimension reduction used to visualize the most essential expression profiles inherent in the data.