Sentence examples for robust partition from inspiring English sources
We find that the edge information obtained through the global orientation energy edge detection (OEED) on an initially deblocked image provides a robust partition of local directional features (LDFs).
When this encoding is used for hierarchical density-based clustering, an especially robust partition of the input sequences is obtained, which a posteriori corroborates its relevance.
Instead of requiring two successive steps ('denoising' followed by clustering), we have shown here that the clustering process itself can be used at the same time to produce a robust partition (insensitive to sequencing errors) and to identify the outlier sequences, i.e. those that were pooled as 'noise' in this article.
Our results show that this approach leads to especially robust partitions without being detrimental to the overall accuracy.
Noticeably, etr-04 was previously shown to be one of the most robust partitions produced by ESPRIT-Tree (Fig. 5).
By combining the sensitive Tag-seq method with the GNS/NS model system we obtain a highly robust partitioning of malignant and normal cell populations, and identify candidate oncogenes and tumor suppressors not previously associated with glioma.
Combinations of ATs and BT give most species; the results are robust to partitioning of effort between both methods.
In order to limit the number of partitions, we propose a robust and fast partitioning algorithm, called modified Bayesian adaptive resonance theory (MB-ART) partition, based on Bayesian ART neural network architecture.
Threshold values selected within this trough are robust and accurately partition the network when compared to gold standards.
Simulation of this revised model, named the 'DNA-relay' model hereafter, showed that considering the DNA as a dynamic matrix has a dramatic effect, yielding fast and robust translocation of partition complexes.
While our method also employs the harmonic field for segmentation, it fully exploits the dental characteristics (see Section 3.3), which makes the proposed method smart, accurate, and more robust in tooth partition.
