Sentence examples for many different datasets from inspiring English sources

The computer vision system based on the proposed approach was tested on many different datasets, demonstrating the generalizability of the proposed approach thanks to the strong performance recorded.

This article largely extends our previous work by comparing a much larger set of methods across many different datasets, providing a much deeper benchmark evaluation of current popular sentiment analysis methods.

In the case of post-eruption impact assessment data, there may be many different datasets available for numerous eruptions and locations, all of which could be of variable detail and quality.

Different from the results reported in [21] using (n = 10,000) points under the uniform distribution, as shown in Fig. 6, with many different datasets, (mathsf {SV}) is not the best, even though (mathsf {SV}) like other edge-based heuristics can get better accuracy.

The overall results suggest that the benchmark gives a fair judgement of the relative performance of the methods, but the score variations again demonstrate the importance of evaluating methods on many different datasets.

Robust coexpression analysis depends upon sampling many different datasets.

While the pleiotropic control of multiple genes by a genetic locus, called an eQTL hotspot, has been previously reported in analyses of many different eQTL datasets, our analysis of the yeast eQTL dataset revealed another layer of complexity in gene regulation by uncovering the pleiotropic effect of multiple genetic loci on multiple genes.

Generating new therapeutic hypotheses for human disease requires the analysis and interpretation of many different experimental datasets.

These kinds of interactions are described in different ways in many different Chem2Bio2RDF datasets: PubChem BioAssay, ChEMBL and BindingDB contain binding experiments; PharmGKB contains genetic variations upon drug response; CTD and Array Express contain expression data; KEGG contains interactions in pathways.

As pointed out in a comprehensive review [ 34], there is no need to separate a benchmark dataset into a training dataset and a testing dataset for validating a prediction method if it is tested by the jackknife or subsampling (K-fold) cross-validation because the outcome thus obtained is actually from a combination of many different independent dataset tests.

EPSA can be applied to many different types of datasets in spite of different platforms, different experimental designs, and different species.