Filter and refine

Filter and Refine Principle (FRP) is a general computational strategy in <a href="/facts/Computer_science/lN3pEyPz">computer science</a>.
FRP is used broadly across various disciplines, particularly in <a href="/facts/Information_retrieval/MFSmpeNw">information retrieval</a>, <a href="/facts/Database_management/VYcpkevb">database management</a>, and <a href="/facts/Pattern_recognition/o1s38opj">pattern recognition</a>, which efficiently processes large sets of objects through a two-stage approach: filtering and refinement.
The filtering stage quickly eliminates less promising or irrelevant objects from a large set using efficient, less resource-intensive algorithms. This stage is designed to reduce the volume of data that needs to be processed in the more resource-demanding refinement stage.
Following filtering, the refinement stage applies more complex and computationally expensive techniques to the remaining objects to achieve higher accuracy via finer-grained processing. This stage is essential for obtaining the desired quality and precision in the results.
FRP is a general method for completing a computationally intensive task as quickly as possible (<a href="https://www.researchgate.net/publication/249590687_Filter_and_Refine_Strategy">Filter and Refine Strategy</a>), which is important in scenarios where managing the inherent trade-offs between speed and accuracy is crucial. Its implementations span various fields and applications, from <a href="/facts/Database_index/TRqmk9It">database indexing</a>/query processing, and <a href="/facts/Information_retrieval/MFSmpeNw">information retrieval</a> to <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a> and <a href="/facts/Big_data/XGtrVg4z">big data</a> analytics. Its implementation helps in optimizing systems to better manage the inherent trade-offs between speed and accuracy.

Filter and refine open-in-new

Filter and refine