Sentence examples for many more samples from inspiring English sources

By involving the fishers, many more samples can be taken, giving more insight in spatial and temporal variation.

MILLIONS of people receive blood transfusions in this country every year, and many more samples of potentially diseased blood are handled by clinical workers in laboratories.

"The choice was much larger in the sense that with the same parameters of price, many more samples were received," said Mr. Spurrier, a leading British wine expert and writer.

The ensuing correspondence lasted for years, with the poet sending her "preceptor," as she called him, many more samples of her work.

"Of course, they would bring back many more samples as well.

To find rare cancer genes, researchers need to sequence many more samples, he said.

In India many more sampling points have been established, and in all countries better survey methods, relying on surveyors knocking on doors rather than asking questions at clinics, have gathered data from more representative samples of the population.Sceptics will feel vindicated by the revision.

In India many more sampling points have been established, and in all countries better survey methods, relying on surveyors knocking on doors rather than asking questions at clinics, have gathered data from more representative samples.Sceptics will feel vindicated by the revision.

However, a preliminary survey of the population must then be made before sampling starts, and it may be necessary to visit many more sampling units to obtain the required sample than is the case when a list is available.

However, when Iles' methods are used, a preliminary survey of the population must be made before sampling starts and it may be necessary to visit many more sampling units to obtain the required sample than is the case when a list is available.

This shows that an ensemble classifier built from this collection of bags--which is called a bagging classifier--improves prediction accuracy by embracing (i.e., reducing the influence of) noisy samples, as long as there are many more "good" samples than "bad" samples in the original training set S, which is a reasonable assumption for any decent training datasets.