Sentence examples for large set of items from inspiring English sources

Bloom filter (BF) is a space-efficient data structure that represents a large set of items and supports efficient membership queries.

Especially in studies involving a large set of items or sample, this formula will prove useful to perform the bootstrap in a reasonable amount of time.

The basic version of the original problem is as follows: given a (large) set of items, a predicate, and a number k, use humans to find k items from the given set that satisfy a given predicate.

Consider the case of information overload, when one is presented with a large set of items.

The CTRS-R was developed by factor analyzing a large set of items, and including items that load highly on interpretable common factors.

In the present study, in order to select concept-property items on modality-exclusivity, we obtained modality ratings for a large set of items.

However, it also measures the size of the largest set of items that are not only recommended but also mutually reachable and, therefore, discoverable.

By considering these threshold values, we verified the impact of different sizes of term sets, as well as what types of terms should be regarded: those more specific, i.e., that may appear in at least two items (threshold 1) or those more general, i.e., that may appear in a larger set of items (threshold 200).

In this development process, the first analytic objective was to reduce the larger set of items to a smaller set, such that this smaller set of items would adequately represent the factor structure.

For cross-sectional use scales should have a wide range of items, should be longer, and there are no adverse floor and ceiling effects, and response options can be simpler to allow a larger set of items.

It is theoretically plausible that items might operate differently when tested within a larger set of items (i.e. 17 items within a pool of 42 tested items) due to learning or fatigue effects compared to the final set of 15 item DEMMI.