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- W4288390009 abstract "<tex>$k-text{Nearest}$</tex> Neighbor <tex>$(k-text{NN})$</tex> is a well-known instance-based learning algorithm; widely used in pattern recognition. A <tex>$k-text{NN}$</tex> classifier can generate highly accurate predictions if provided with sufficient training instances. Thus, it plays a pivotal role in many fields. However, though its accuracy can improve with more data, the need for computational resources increases as well. In this paper, we propose a novel approach which pre-partitions instance space into smaller cells in order to reduce computational cost and greatly reduce the time complexity. Assume every instance is mapped to a point in the <tex>$d-text{dimensional}$</tex> Euclidean space and a training set <tex>$D$</tex> contains <tex>$n$</tex> training instances. Given a query instance, the brute force <tex>$k-text{NN}$</tex> algorithm has the <tex>$O(nd)$</tex> time complexity for predicting the class of a query instance. This algorithm can improve the time complexity." @default.
- W4288390009 created "2022-07-29" @default.
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- W4288390009 date "2022-05-10" @default.
- W4288390009 modified "2023-10-16" @default.
- W4288390009 title "The Fast $k-text{NN}$ Algorithm Based on a Fixed Grid Method" @default.
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- W4288390009 doi "https://doi.org/10.1109/airc56195.2022.9836452" @default.
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