Abbreviated Journal Title
Neural Comput.
Keywords
DENSITY; CLASSIFICATION; Computer Science, Artificial Intelligence
Abstract
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 crossvalidation 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.
Journal Title
Neural Computation
Volume
19
Issue/Number
10
Publication Date
1-1-2007
Document Type
Article
Language
English
First Page
2840
Last Page
2864
WOS Identifier
ISSN
0899-7667
Recommended Citation
Zhong, Mingyu; Coggeshall, Dave; Ghaneie, Ehsan; Pope, Thomas; Rivera, Mark; Georgiopoulos, Michael; Anagnostopoulos, Georgios C.; Mollaghasemi, Mansooreh; and Richie, Samuel, "Gap-based estimation: Choosing the smoothing parameters for Probabilistic and general regression neural networks" (2007). Faculty Bibliography 2000s. 28.
https://stars.library.ucf.edu/facultybib2000/28
Comments
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