Sentence examples for metrics for which from inspiring English sources

The similarity metric is defined as linear combination of single feature space metrics for which the corresponding weights are learned from a group of representative salient blocks using multi-objective optimisation.

However, the opposite was the case for several of the metrics for which a predictable scaling behaviour had been previously described.

Specifically, Melnikov theory is investigated to provide metrics for which homoclinic bifurcation may occur in the presence of harmonic, multi-frequency, and broadband excitation.

We also provide computational results illustrating the performance of our approach for different types of metrics, including l1-distances and two-decomposable metrics for which it is provably possible to find optimal realizations in their tight spans.

We considered eight landscape pattern metrics for which predictable scaling functions have been reported, and compared the subpixel estimates provided by those scaling functions (when fitted to the metric values for different ranges of spatial resolution above the pixel level) with the true value of the metric at the subpixel resolution.

Additionally, there are also metrics for which computations performed in the linear and logarithm domains perform better than in the PU and PQ space.

Evaluation criteria and metrics were: For which inputs the tool was not able to generate test cases?

On the other hand, f ∈ H 1 ( M ) implies N g f ∈ H ~ 2 ( M 1 ) despite the possible jump of f at ∂ M. Our stability estimate for the linearized inverse problem is as follows: There exists k 0 such that for each k ≥ k 0, the set G k ( M ) of simple C k ( M ) metrics in M for which I g is s-injective is open and dense in the C k ( M ) topology.

We observed that the simple presence-/absence-based metric Dcount performed at least as well as abundance-sensitive log and sqrt metrics, except for the MetaHIT data for which the other metrics performed better.

Along with this, we discuss which metrics are most appropriate for which scenarios in order to evaluate the models.

Fourth, using of modern visual quality metrics, we determine that for which levels of i.i.d. and spatially correlated noise the noise in original images or residual noise and distortions because of filtering in output images are practically invisible.