Sentence examples for partition input from inspiring English sources

Unsupervised clustering tries to partition input data into a number of groups smaller than the number of data whereas supervised machine learning tries to construct model functions that map the input data onto their corresponding output data.

Distributed graph engines commonly (1) partition input graph data into sub-graphs, (2) assign each sub-graph to each computer, and (3) make graph analysis over the distributed graph.

Thus, we also provide the boundaries between genes in text files (these can be directly used as partition input files to RAxML).

It automatically partitions input data, schedules execution across the cluster, and handles nodes failures.

If both solutions are combined, establishing a task pipeline and partitioning input images, an easily scalable and flexible scheme is obtained.

It automatically partitions input ligands, prepares parameter files for AutoDock, launches parallel AutoDock runs, parses results, and saves a set of top-ranking docked ligands.

In order to achieve parallelism wherever applicable relies on partitioning input string into smaller segments and construct subtrees for each of such segment.

The NCR technique exploits parallelism by partitioning input wavefronts, such that one circuit processes a DATA wavefront, while its duplicate processes a NULL wavefront.

The distributed graph engines execute analysis process after partitioning input graph data and assign them to distributed computers, so the quality of graph partitioning largely affects the communication cost and load balance among computers during the analysis process.

The salient characteristics of the AVS-FNN are as follows: (1) Structure-learning and parameters estimation are performed automatically and simultaneously without partitioning input space and selecting initial parameters a priori.

This layer is responsible for: (1) checkpointing tasks to account for task failure and allow restarts in remote nodes, without any source code modification, through the use of VMs; (2) providing different options for partitioning input data, chosen by the user; (3) local sampling at the server (more details afterwards in this section), for validation purposes.