Adaptive Kernel Smoothing Regression for Spatio-Temporal Environmental Datasets
Abstract
A Method for Performing Kernel Smoothing Regression in an Incremental, Adaptive Manner is Described. a Simple and Fast Combination of Incremental Vector Quantization with Kernel Smoothing Regression using Adaptive Bandwidth is Shown to Be Effective for Online Modeling of Environmental Datasets. the Approach Proposed is to Apply Kernel Smoothing Regression in an Incremental Estimation of the (Evolving) Probability Distribution of the Incoming Data Stream Rather Than the Whole Sequence of Observations. the Method is Illustrated on Publicly Available Datasets Corresponding to the Tropical Atmosphere Ocean Array and the Helsinki Commission Hydrographic Database for the Baltic Sea. © 2012 Elsevier B.v.
Recommended Citation
F. Montesino Pouzols and A. Lendasse, "Adaptive Kernel Smoothing Regression for Spatio-Temporal Environmental Datasets," Neurocomputing, vol. 90, pp. 59 - 65, Elsevier, Aug 2012.
The definitive version is available at https://doi.org/10.1016/j.neucom.2012.02.023
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Adaptive regression; Environmental applications; Evolving intelligent systems; Kernel smoothing regression; Spatio-temporal models; Vector quantization
International Standard Serial Number (ISSN)
1872-8286; 0925-2312
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
01 Aug 2012
Comments
Seventh Framework Programme, Grant 237450