Efficient Locally Weighted Polynomial Regression Predictions

Efficient Locally Weighted Polynomial Regression Predictions (0)

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by Andrew W. Moore , Jeff Schneider , Kan Deng

Venue:	In Proceedings of the 1997 International Machine Learning Conference
Citations:	71 - 11 self

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Datum	Value	Source
TITLE	Efficient Locally Weighted Polynomial Regression Predictions	user correction - Legacy Corrections
AUTHOR NAME	Andrew W. Moore	user correction - Legacy Corrections
AUTHOR AFFIL	Robotics Institute and; School of Computer Science; Carnegie Mellon University	user correction - Legacy Corrections
AUTHOR ADDR	Pittsburgh, PA 15213	user correction - Legacy Corrections
AUTHOR NAME	Jeff Schneider	user correction - Legacy Corrections
AUTHOR AFFIL	Robotics Institute; Carnegie Mellon University	user correction - Legacy Corrections
AUTHOR ADDR	Pittsburgh, PA 15213	user correction - Legacy Corrections
AUTHOR NAME	Kan Deng	user correction - Legacy Corrections
AUTHOR AFFIL	Robotics Institute; Carnegie Mellon University	user correction - Legacy Corrections
AUTHOR ADDR	Pittsburgh, PA 15213	user correction - Legacy Corrections
ABSTRACT	Locally weighted polynomial regression (LWPR) is a popular instance-based algorithm for learning continuous non-linear mappings. For more than two or three inputs and for more than a few thousand datapoints the computational expense of predictions is daunting. We discuss drawbacks with previous approaches to dealing with this problem, and present a new algorithm based on a multiresolution search of a quicklyconstructible augmented kd-tree. Without needing to rebuild the tree, we can make fast predictions with arbitrary local weighting functions, arbitrary kernel widths and arbitrary queries. The paper begins with a new, faster, algorithm for exact LWPR predictions. Next we introduce an approximation that achieves up to a two-ordersof -magnitude speedup with negligible accuracy losses. Increasing a certain approximation parameter achieves greater speedups still, but with a correspondingly larger accuracy degradation. This is nevertheless useful during operations such as the early stages...	user correction - Legacy Corrections
YEAR	0	user correction - Legacy Corrections
VENUE	In Proceedings of the 1997 International Machine Learning Conference	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	236--244	INFERENCE
CITATIONS	14 found	ParsCit 1.0