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- W3133463140 abstract "The dimensions of data in the real world are often relatively high. Traditional indexing methods are difficult to deal with complex situations. How to efficiently and effectively retrieve data has become a hot topic. In recent years, with the development of artificial intelligence, machine learning has played a huge role in many fields. On this basis, Google proposed a new idea of using machine learning models to replace the traditional index structure and proposed a learned index structure. The learned KD tree is based on the learned index architecture, using a neural network model instead of the traditional tree structure to explore the nearest neighbor search. Through theoretical analysis, frame designed, and experiments, it is preliminarily shown that the learned KD tree can obtain a certain improvement in search time while ensuring search accuracy." @default.
- W3133463140 created "2021-03-01" @default.
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- W3133463140 date "2020-12-01" @default.
- W3133463140 modified "2023-09-29" @default.
- W3133463140 title "A Study of Learned KD Tree Based on Learned Index" @default.
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- W3133463140 doi "https://doi.org/10.1109/nana51271.2020.00067" @default.
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