Sentence examples for queried item from inspiring English sources

Traditionally in content-based classification and retrieval scheme, specific similarity measures, such as Euclidean distance, are applied to measure the distances between the FVs of a classified (or queried) item and each item belonging to the database.

Finally, before moving to the next database item, we take the sum of all the obtained minimum distances to obtain a single distance value between the queried item and the database item (which is used to rank that query item).

Note that it will be necessary for Data Managers to respond to each inquiry received in order to obtain closure on the queried item.

Sometimes, even if the input vector is large, pair-wise ratings between the queried for item and the items in the input vector may not exist, which will reduce the prediction time.

When the BS requires the provenance of a received data item, it has to send a query to the entire network and then retrieve the provenance by combining the responses from those nodes which have provenance information for the queried data item.

Second, we take the next FV of the query item and continue similarly with all the query item FVs.

With this achievement, web pages could be employed as a query item to find other, similar web pages by taking into consideration that they are web pages, instead of images or anything else.

However, neither option would be computationally feasible for large databases, as they both require conducting a separate batch query (i.e., selecting each item in the database as a query item one by one, performing a separate query for each of them, and finally taking the mean of the obtained retrieval results) for every fitness evaluation during the synthesis process.

In our approach, we first compute (using Euclidean distance) the distances between the first FV of a query item and all the FVs of a particular database item from which we take the minimum distance (normalized by the vector length) and store it.

cTotal annotated query item number in agriGO.

To say that an interaction A: B is analogous to S={ A(1): B(1), A(2): B(2),…, A(N ): B(N )} amounts to implicitly defining a measure of similarity between the pair A: B and the set of pairs S, where each query item A k ): B k ) corresponds to some pair A i : B j.