NDLI: A new approach to cluster-weighted modeling

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A new approach to cluster-weighted modeling

Content Provider	IEEE Xplore Digital Library
Author	Prokhorov, D.V. Feldkamp, L.A. Feldkamp, T.M.
Copyright Year	2001
Description	Author affiliation: Res. Lab., Ford Motor Co., Dearborn, MI, USA (Prokhorov, D.V.)
Abstract	We discuss an approach to joint density estimation called cluster-weighted modeling (CWM). The base approach was originally proposed by Gershenfeld (1998). We describe two innovations to the base CWM. Among these, the first enables the CWM to work with continuous streams of data. The second addresses the commonplace problem of local minima which may be encountered during the CWM parameter adjustment process. Our approach to mitigate this problem is quite elaborate, but it represents a principled way of improving the efficacy of the parameter adjustment process. We illustrate CWM and our performance enhancements with an example.
Sponsorship	Int. Neural Network Soc.
Starting Page	1669
Ending Page	1674
File Size	562914
Page Count	6
File Format	PDF
ISBN	0780370449
ISSN	10987576
DOI	10.1109/IJCNN.2001.938412
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gaussian processes Density measurement Technological innovation Density functional theory Nonhomogeneous media Training data Collaborative work Optimization methods Covariance matrix Interpolation
Content Type	Text
Resource Type	Article

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