Title

Gap-Based Estimation: Choosing The Smoothing Parameters For Probabilistic And General Regression Neural Networks

Abstract

Probabilistic Neural Networks (PNN) and General Regression Neural Networks (GRNN) represent the knowledge by a simple but interpretable model that approximates the optimal classifier/predictor in the sense of expected value of accuracy. This model requires an important preset smoothing parameter, which is usually chosen by cross-validation or clustering. In this paper, we demonstrate the difficulties of both these approaches, 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 to the compared approaches. © 2006 IEEE.

Publication Date

1-1-2006

Publication Title

IEEE International Conference on Neural Networks - Conference Proceedings

Number of Pages

1870-1877

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ijcnn.2006.246908

Socpus ID

40649086216 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/40649086216

This document is currently not available here.

Share

COinS