Sentence examples for likelihood of classification from inspiring English sources

In addition, the absence of running movements decreased the likelihood of classification as a focal seizure with secondary generalisation 0.17 fold vs. a focal seizure (p = 0.004).

Reports of thoracic limb stiffening were associated with classification of primary generalised seizures, with their absence increasing the likelihood of classification as a focal seizure 7.83 fold vs. a primary generalised seizure (p < 0.001), and decreasing the likelihood of classification as a focal seizure with secondary generalisation 0.19 fold vs. a focal seizure (p < 0.001).

Finally, reports of hallucination were associated with the classification of focal seizures, with reports of absence of hallucination decreasing the likelihood of classification as a focal seizure 0.24 fold vs. a primary generalised seizure (p = 0.029).

Reports of running movements were associated with classification as a primary generalised seizures and focal seizures with secondary generalisation, with their absence increasing the likelihood of classification as a focal seizure 9.75 fold vs. a primary generalised seizure (p < 0.001).

Reports of salivation were associated with the classification of primary generalised seizures, with reports of absence of salivation increasing the likelihood of classification as a focal seizure 2.69 fold vs. a primary generalised seizure (p = 0.009).

Reports of rhythmic thoracic limb movements were also associated with classification of primary generalised seizures, with their absence increasing the likelihood of classification as a focal seizure 3.7 fold vs. a primary generalised seizure (p < 0.001).

All there should be instead for noisy and/or genuinely pseudo-symmetric images are rankings of the relative likelihoods of classifications into higher symmetric Bravais lattice types, Laue classes, and plane symmetry groups.

In these cases, the likelihood of the classification is equal to the maximum likelihood, and the AIC-based test will always favor the classification over the continuous model because of the difference in degrees of freedom.

That is, as the SNR decreases, the likelihood of correct classification is always in favor of 64QAM.

A useful theorem, found in (Weston et al. 2000) but proven, in effect, in (Vapnik 1998) relates the likelihood of erroneous classification to the radius found by (3).

Results indicated that there were strong differences in the likelihood of resistance classification for NTSEC isolates from individual fecal samples for 5 of 6 AMDs (Table  5).