It is a challenge to accurately estimate user density for areas with poor signal strength. However, user density can be estimated from other big data collected ...
An adjusted K-Nearest-Neighbor is applied to infer bad coverage user densities from the good coverage areas. Instead of predefining the K, different percentile ...
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What is kNN based density estimation?
How can I improve my kNN model?
Aug 11, 2024 · kNN in low-dimensional space: an overview. Basic kNN search approach ... improve performance on imbalanced datasets, such as resampling methods or ...
Missing: poor | Show results with:poor
... poor because they use a simple ratio of integers to estimate probabilities. To improve the quality ... KNN is a non-parametric algorithm, meaning it makes no ...
Missing: signal | Show results with:signal
Mar 2, 2022 · Auxiliary data can be used to improve estimator efficiency through model-assisted estimation ... Use of remote sensing data to improve ...
Missing: poor signal strength.
Sep 30, 2020 · kNN density estimator for distributions with bounded support does not improve even if the second ... region with low density, since the number of ...
Missing: poor signal
Apr 8, 2019 · I have no idea how to even approximate that integral, but it at ... Lowest processable signal level after FFT with given noise level.
Missing: small strength.
... region with high density and that with low density. This explains the gap ... To improve the performance of the kNN density estimator, we design an adaptive ...
Missing: poor signal
In kNN, the rate of variance stays the same rate but the rate of bias changes with respect to the dimension. One intuition is that no matter what dimension ...
Missing: Adjusted | Show results with:Adjusted
Mar 9, 2019 · It is often used to solve nonlinear problems, such as credit ratings and bank customer rankings, in which the collected data do not always ...