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- W2113991633 abstract "We illustrate how a KD tree data structure with Delaunay triangulation can be used for function learning. The example function is the inverse kinematics of a three degree-of-freedom (DOF) robot. The result can subsequently be used for kinematic control. The KD tree is used to efficiently extract a set number of nearest neighbors to a query point. Delaunay triangulation provides a good criterion for constructing a continuous linear approximation to the true function from neighborhood points of the query. For comparison purposes we solve the same problem with a neural network trained with error backpropagation. We conclude that the KD/Delaunay approach, in comparison to neural networks, can potentially yield a massive reduction in training time and significantly improve function estimate performance." @default.
- W2113991633 created "2016-06-24" @default.
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- W2113991633 date "1996-01-01" @default.
- W2113991633 modified "2023-09-23" @default.
- W2113991633 title "KD trees and Delaunay-based linear interpolation for function learning: a comparison to neural networks with error backpropagation" @default.
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- W2113991633 doi "https://doi.org/10.1109/87.541694" @default.
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