In *Neural Networks, 2006. IJCNN '06. International Joint Conference on*, pages 1870-1877, 2006.

doi abstract bibtex

doi abstract bibtex

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.

@InProceedings{Zhong2006, author = {Zhong, M. and Coggeshall, D. and Ghaneie, E. and Pope, T. and Rivera, M. and Georgiopoulos, Michael and Anagnostopoulos, Georgios C. and Mollaghasemi, Mansooreh and Richie, S.}, title = {Gap-Based Estimation: Choosing the Smoothing Parameters for Probabilistic and General Regression Neural Networks}, booktitle = {Neural Networks, 2006. IJCNN '06. International Joint Conference on}, year = {2006}, pages = {1870-1877}, 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.}, doi = {10.1109/IJCNN.2006.246908}, keywords = {knowledge representation;neural nets;regression analysis;data statistics;gap-based estimation;general regression neural networks;knowledge representation;preset smoothing parameter;probabilistic neural networks;smoothing parameters;Computer science;Engineering management;Industrial engineering;Kernel;Linear discriminant analysis;Neural networks;Predictive models;Smoothing methods;Statistics;Training data}, }

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