Sentence examples for the characterization of data from inspiring English sources

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Results show that with the characterization of data, through statistical, information and complexity measures, it is possible to reach an accuracy higher than 90%.

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Many decisions in the process of scientific inquiry may conceal implicit value judgments: the design of an experiment, the methodology for conducting it, the characterization of the data, the choice of a statistical method for processing and analyzing data, the interpretational process findings, etc.

"That statement is not correct when applied to the totality of these files," Mr. Glauthier said of the characterization of the data.

However, the fluorescence intensity is not a convenient variable for the characterization of kinetic data; the initial reaction rate better reflects the enzyme efficiency.

We highlight the key input parameters for risk assessments and consider how modest changes in the characterization of these data might enable more accurate risk assessments that better represent the findings of epidemiological studies.

Supporting the characterization of the GNSS data with C/NOFS data, we noted that small-spatial scale density depletions are more detrimental to GNSS signals than their large-scale counterparts.

Mining of security or authorization policies based on an available static access control matrix has been within the focus of research for some time (e.g. [18, 36]) especially since standards like ABAC [37] are heavily dependent on this construct, like this aim to extend these techniques by enhancing the input data and the characterizations of output data using contextual information.

To introduce kriging, one needs to get familiar with the spatial characterization of data.

The functional characterization of data are presented in Figure 3, which lists top ten canonical pathways regulated by estrogen across both tissue types.

We also investigate the self-adjoint case and obtain the characterization of the spectral data as a corollary of our general result.

In other words, the fusion of the L sources of information is performed in a mutual exclusive manner, and not in "collective" way where all the sources contribute (each with a small portion) to the characterization of the raw data.

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