Adaptive Kernel Smoothing Regression for Spatio-Temporal Environmental Datasets

Author

Federico Montesino Pouzols
Amaury Lendasse, Missouri University of Science and TechnologyFollow

Abstract

This Paper Describes a Method for Performing Kernel Smoothing Regression in an Incremental, Adaptive Manner. 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 Method is Illustrated on Openly Available Datasets Corresponding to the Tropical Atmosphere Ocean Array and the Helsinki Commission Hydrographic Database for the Baltic Sea.

Recommended Citation

F. M. Pouzols and A. Lendasse, "Adaptive Kernel Smoothing Regression for Spatio-Temporal Environmental Datasets," ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 87 - 92, European Symposium on Artificial Neural Networks, Dec 2010.

Department(s)

Engineering Management and Systems Engineering

International Standard Book Number (ISBN)

978-287419044-5

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 European Symposium on Artificial Neural Networks, All rights reserved.

Publication Date

01 Dec 2010