Twin Neural Network Improved k-Nearest Neighbor Regression
read the original abstract
Twin neural network regression is trained to predict differences between regression targets rather than the targets themselves. A solution to the original regression problem can be obtained by ensembling predicted differences between the targets of an unknown data point and multiple known anchor data points. Choosing the anchors to be the nearest neighbors of the unknown data point leads to a neural network-based improvement of k-nearest neighbor regression. This algorithm is shown to outperform both neural networks and k-nearest neighbor regression on small to medium-sized data sets.
This paper has not been read by Pith yet.
Forward citations
Cited by 1 Pith paper
-
Fitting scattered data with optional monotonicity constraints on GPU: LipFit package
LipFit package offers GPU-parallel Lipschitz interpolation and smoothing for scattered data with optional monotonicity constraints using tight upper and lower bounds.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.