文章基本信息

标题：A Comparison Between Three Neural Network Models for Classification Problems
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作者：Essam Al-Daoud
期刊名称：Journal of Artificial Intelligence
印刷版ISSN：1994-5450
电子版ISSN：2077-2173
出版年度：2009
卷号：2
期号：2
页码：56-64
DOI：10.3923/jai.2009.56.64
出版社：Asian Network for Scientific Information
摘要：This study reports the results from three artificial neural network models. Levenberg-Marquardt (LM), Generalized Regression Neural Networks (GRNN) and Learning Vector Quantization (LVQ) are applied to eight classification problems. Ten-fold cross validation is used to demonstrate the error rate of networks. The experiments show that the generalized regression neural network s outperform the other classifiers, where the average training performance is 0.0436, the testing error rate is 0.137 and the classification rate is 0.80. On the contrary, by using Levenberg-Marquardt, the average training performance is 0.092, the testing error rate is 0.169 and the classification rate is 0.59 and by using learning vector quantization the average training performance is 0.078, the testing error rate is 0.363 and the classification rate is 0.64.