Nonlinear model selection based on the modulus of continuity

Imhoi Koo, Rhee Man Kil

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The prediction risk estimation in nonlinear regression models including artificial neural networks is especially important for problems with limited data since it can be used as a tool for finding the optimal model (or network architecture) minimizing the expected risk. In this paper, we suggest the prediction risk bounds of nonlinear regression models. The suggested bounds are derived from the modulus of continuity for a multivariate function. We also present the model selection criteria referred to as the modulus of continuity information criteria (MCIC) derived from the suggested prediction risk bounds. Through the simulation for function approximation, we have shown that the suggested MCIC is effective in nonlinear model selection problems with limited data.

Original languageEnglish (US)
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
Pages1886-1893
Number of pages8
StatePublished - Dec 1 2006
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: Jul 16 2006Jul 21 2006

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Other

OtherInternational Joint Conference on Neural Networks 2006, IJCNN '06
CountryCanada
CityVancouver, BC
Period7/16/067/21/06

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Koo, I., & Kil, R. M. (2006). Nonlinear model selection based on the modulus of continuity. In International Joint Conference on Neural Networks 2006, IJCNN '06 (pp. 1886-1893). [1716340] (IEEE International Conference on Neural Networks - Conference Proceedings).