Gap-Based Estimation: Choosing the Smoothing Parameters for Probabilistic and General Regression Neural Networks
Mingyu Zhong, D. Coggeshall, E. Ghaneie, Thomas Pope, Mark Rivera, Michael Georgiopoulos, Georgios C. Anagnostopoulos, Mansooreh Mollaghasemi, S.M. Richie · Neural Computation · 2007
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 article, 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.