Sentence examples for voting datasets from inspiring English sources

Unlike the case of voting datasets a "0" in an association dataset carries relatively little information as opposed to a "1".

We construct BiFold plots for two US voting datasets: For the presidential election outcomes since 1976, BiFold illustrates the evolving geopolitical structures that underlie these election results.

The details of the selected dataset are as follows: In this paper, a Wikipedia voting dataset is used that was gathered from 3 to 31 Jan . 2008

A key feature of such association datasets is that the non-association relations carry little information compared to the association relations; in sharp contrast, in a voting dataset the "yes" and "no" votes both convey valuable information about the relation between the decision makers and the choices.

The U.S. Senate Congressional Voting dataset used in this paper is obtained from the congressional voting records of the 112th United States congress, first session of the Senate.

The rating system was based on a supposed normal distribution of gene expression in different tissues and the final rate was determined by votes from different datasets.

In Bertocchi and Dimico (2012b), my coauthor and I extend the analysis of the political implications of slavery using a unique dataset on voting registration by race assembled for the counties in the state of Mississippi in 1896, in the middle of the period that witnesses the restoration of the white elites' supremacy.

Figure 5 shows the number of files from the test datasets that "vote" for a particular prediction model by producing the most identified spectra at the same q-value.

Genecluster2 then generated blinded predictions on the ALL samples of the test datasets through weighted voting with a leave-one-out methodology.

GeneCluster2 (; Center for Genome Research, MIT, Cambridge, MA) was used to perform blinded predictions on the validation dataset using weighted voting with a leave-one-out methodology.

The experimental results show that there is no bearing on prediction accuracy by choosing different weighting schemes for a majority of microarray datasets, although the training accuracy-based voting and internal cross-validation-based voting performed slightly better (3 5%) than the majority voting scheme on few datasets like the B-cell lymphoma dataset.