Sentence examples for supervised measures from inspiring English sources

The relevance analysis was evaluated using two supervised measures: linear correlation and symmetrical uncertainty.

Therefore, the used supervised distance measure in SLLE is linear.

On one hand, with a nonlinear supervised distance measure, DKLLE considers both the intraclass scatter information and the interclass scatter information in a reproducing kernel Hilbert space (RKHS), and emphasizes the discriminant information.

A discirminant and kernel variant of LLE is developed by designing a nonlinear supervised distance measure and minimizing the reconstruction error in a RKHS, which gives rise to DKLLE.

The classification performance of the supervised algorithms measured by G-Mean and AUC metrics did not significantly change in the four test sets obtained with different alignment parameter settings.

Let Dist denotes the kernel distance matrix for all the input data points, i.e., Dist = Dist(x i, x j ), i, j = 1,2,..., N. To preserve the intraclass neighboring geometry, while maximizing the interclass scatter, a nonlinear supervised kernel distance measure KDist in a RHKS can be defined as: K D i s t = 1 - e - D i s t 2 β L i = L j e D i s t 2 β - α L i ≠ L j (6).

All of these methods, however, are designed for use in a supervised context to measure the statistical significance of the association between sets of genomic variables and a phenotype variable.

In the case of supervised empirical assessments, discrepancy measures are employed for computing measures of dissimilarity between a reference polygon and an image object candidate.

To measure the FMD precisely, 100 independent supervised scans and measurements are required according to a previous report.

Several complexity measures for supervised classification have been designed to define these areas.

In this paper, we want to discover which type of datasets, defined by certain range values of the complexity measures for supervised classification, fits for some of the most well-known semi-naive Bayesian network classifiers.