Investigating the behaviour of radial basis function networks in regression and classification of geospatial data

Authors:
Andrea Guidali;Elisabetta Binaghi;Mauro Guglielmin;Marco Pascale
Affiliations:
Department of Computer Science and Communication, University of Insubria, Varese, Italy;Department of Computer Science and Communication, University of Insubria, Varese, Italy;Department of Structural and Functional Biology, University of Insubria, Varese, Italy;Department of Computer Science and Communication, University of Insubria, Varese, Italy
Venue:
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Year:
2010

Citing 4
Cited 0

Learning internal representations by error propagation

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Artificial Neural Networks: A Tutorial

Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Neural networks for pattern recognition

Neural networks for pattern recognition
Fast learning in networks of locally-tuned processing units

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work investigates learning and generalisation capabilities of Radial Basis Function Networks used to solve function regression and classification tasks in the environmental context. In particular RBFN is applied to solve the problem of snow cover thickness estimation in which critical aspects such as minimal training condition, weak pattern description and inconsistency among data arise. The RBFN shows good performances and high flexibility in coping with regression, hard and soft classifications which are complementary tasks in the analysis of complex environmental phenomena.