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Gap-Based Estimation: Choosing the Smoothing Parameters for Probabilistic and General Regression Neural Networks

myzhong{at}ucf.edu School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, U.S.A.

Dave Coggeshall

david.coggeshall{at}gmail.com School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, U.S.A.

Ehsan Ghaneie

Ehsan.Ghaneie{at}gmail.com School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, U.S.A.

Thomas Pope

ThomasPope{at}gmail.com School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, U.S.A.

Mark Rivera

mark.rivera{at}gmail.com School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, U.S.A.

Michael Georgiopoulos

michaelg{at}mail.ucf.edu School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, U.S.A.

Georgios C. Anagnostopoulos

georgio{at}fit.edu Department of Electrical and Computer Engineering, Florida Institute of Technology, Melbourne, FL 32901, U.S.A.

Mansooreh Mollaghasemi

mollagha{at}mail.ucf.edu Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, U.S.A.

Samuel Richie

richie{at}mail.ucf.edu School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, U.S.A.

Probabilistic neural networks (PNN) and general regression neural networks (GRNN) represent knowledge by simple but interpretable models that approximate the optimal classifier or predictor in the sense of expected value of the accuracy. These models require the specification of an important smoothing parameter, which is usually chosen by cross-validation or clustering. In this letter, we demonstrate the problems with the cross-validation and clustering approaches to specify the smoothing parameter, discuss the relationship between this parameter and some of the data statistics, and attempt to develop a fast approach to determine the optimal value of this parameter. Finally, through experimentation, we show that our approach, referred to as a gap-based estimation approach, is superior in speed to the compared approaches, including support vector machine, and yields good and stable accuracy.