التفاصيل البيبلوغرافية
| العنوان: |
Gap-based estimation: choosing the smoothing parameters for probabilistic and general regression neural networks. |
| المؤلفون: |
Zhong M; School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA. myzhong@ucf.edu, Coggeshall D, Ghaneie E, Pope T, Rivera M, Georgiopoulos M, Anagnostopoulos GC, Mollaghasemi M, Richie S |
| المصدر: |
Neural computation [Neural Comput] 2007 Oct; Vol. 19 (10), pp. 2840-64. |
| نوع المنشور: |
Journal Article; Research Support, U.S. Gov't, Non-P.H.S. |
| اللغة: |
English |
| بيانات الدورية: |
Publisher: MIT Press Country of Publication: United States NLM ID: 9426182 Publication Model: Print Cited Medium: Print ISSN: 0899-7667 (Print) Linking ISSN: 08997667 NLM ISO Abbreviation: Neural Comput Subsets: MEDLINE |
| أسماء مطبوعة: |
Original Publication: Cambridge, Mass. : MIT Press, c1989- |
| مواضيع طبية MeSH: |
Models, Statistical* , Neural Networks, Computer*, Cluster Analysis |
| مستخلص: |
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. |
| تواريخ الأحداث: |
Date Created: 20070825 Date Completed: 20071030 Latest Revision: 20191210 |
| رمز التحديث: |
20240628 |
| DOI: |
10.1162/neco.2007.19.10.2840 |
| PMID: |
17716014 |
| قاعدة البيانات: |
MEDLINE |
