Sentence examples for data-driven validation from inspiring English sources

The resolution in all five track parameters is controlled with data-driven validation of the track parameter measurements near the interaction region, and tested against prediction with detailed detector simulation.

Although this relationship has been previously studied in cell culture and in animal models, the patient-specific PIRT mathematical model enabled the data-driven validation of this radiation biology truism in human data.

In conclusion, our experiments show that FC is quite close in time performance to three of the fastest data-driven validation measures available in the literature, while also granting better precision results.

Based on the above considerations, this paper focuses on data-driven internal validation measures, particularly on those designed for and tested on microarray data.

In view of the fact that the former measures are considered reference points in this area, the speed-up of Consensus proposed here seems to be a non-trivial step forward in the area of data-driven internal validation measures.

One open question that was made explicit by the study of Giancarlo et al. is the design of a data-driven internal validation measure that is both precise and fast, and capable of granting scalability with dataset size.

The results outlined above are particularly significant since (i) FOM is one of the most established and highly-referenced measures specifically designed for microarray data; (ii) in purely algorithmic terms, WCSS and G-Gap, are so simple as to represent a "lower bound" in terms of the time performance that is achievable by any data-driven internal validation measure.

In terms of the existing literature on data-driven internal validation measures, we have that, by extending the benchmarking results of Giancarlo et al., FC is only one order of magnitude away from the fastest measures, i.e., WCSS, yet granting a superior performance in terms of precision.

Therefore, to overcome the above problem, we propose the data-driven cross-validation approach to select an optimal λ.

The other accepted practice is to follow a data-driven cross-validation (CV) procedure.

The r8s PL approach uses a data-driven cross-validation procedure to select an appropriate level of rate smoothing given branch length estimates proportional to substitution differences.