Sentence examples for an arbitrary feature from inspiring English sources

"Given these facts, Tucker may well have received the death penalty not because of the comparative egregiousness of his crime, but because of an arbitrary feature of his case, namely, geography," Breyer wrote, in a dissent joined by Ginsburg.

Therefore, the weight of an arbitrary feature can be learned from its global interactions with all the other features.

Please note that even though in the gene selection algorithm we refer to genes, the algorithm is generic and in general a 'gene' should be thought of as an arbitrary feature.

Let {(x i, y i )}∈ X × Y, i=1,…, n be a set of data points where X∈ℝ D is an arbitrary feature space and Y a finite set of labels.

In order to invert the quantization process, an adversary who possesses the biohash (b) of a valid system user and corresponding random projection matrix (R) uses an arbitrary biometric feature vector x f to simulate the biometric hashing procedure through random projection and obtain an intermediate vector z f =R x f.

By transforming the initial datasets into a unified phantom set the effect of arbitrary feature covariance due to noise is confined in this layer of organization, downgrading their impact in the unified dataset and thus disqualifying them from candidates for the predictor set.

"We have a very good asylum system," said Philip G. Schrag, a Georgetown law professor who is an author of the study, "but Congress introduced this particularly arbitrary feature that has resulted in the rejection of thousands of cases that would otherwise have been granted".

Additionally, although with low computational complexity, the LS pairing strategy is effective for arbitrary feature densities.

Additionally, it is applicable to arbitrary feature types and independent of the number of quantization bits.

Note that, to do this, we must be able to assign misclassification probabilities to arbitrary feature subsets.

We did not consider de novo feature selection as part of our training process because feature selection consumes statistical (i.e. training) power, and arbitrary feature selection is nondeterministic polynomial-time hard (NP) in the general case (Garey and Johnson, 1979).