Sentence examples for linear ranking from inspiring English sources

LIBLINEAR and the linear ranking SVM use the dot product kernel which generally increases the ranking error or reduces the AUC, since the fingerprints of larger molecules result in higher similarities.

SVMRank learns a linear ranking function f(x)=w T x.

A linear ranking method with selective pressure of two is used for fitness values.

Afterwards, we present the modified linear ranking SVM, which is able to learn various activity profiles.

The standard linear SVM, the linear ranking SVM, and the MC-SVM require the regularization parameter C for training.

Therefore, the implementation of the linear ranking SVM and the experimental setup can be regarded as sound.

To test these hypotheses, we developed a linear rank statistic.

The asymptotic normality of a linear combination of dependent linear rank score statistics is shown under rather weak assumptions.

This provides a general two-sample linear rank statistic, as follows: T = ∑ j = 1 N a ( j ) V j = ∑ j = 1 n 2 a ( R j ).

For the comparison of crossing survival curves, the chapter presents three different major approaches: (1) a modified Kolmogorov Smirnov test by Fleming et al., (2) a Levene-type test aimed directly at crossing-curve alternatives, and (3) a subclass of linear rank tests where locally most powerful linear ranks can be formed by proper choices of an optimal score function.

The direct generalization of linear rank tests to the censored data situation is conceptually simple, but derivations of variance formulas are difficult, even in the cases of the linear score function and the logarithmic score function.