Training neural pattern classifiers with a mean field theory learning algorithm

Jung-Hsien Chiang, Russell Pimmel

研究成果: Paper同行評審

摘要

This study evaluated the mean field theory (MFT) learning algorithm with pattern classifiers and compared its performance to that of the back-propagation (BP) learning algorithm. The deterministic MFT learning algorithm replaced the time-consuming stochastic Boltzmann machine learning algorithm. In our simulation studies, the MFT was superior to the BP learning algorithm with two nonlinearly separable binary mappings, with the Iris data classification problem, and with a simulated signal classification problem. The MFF algorithm always required a substantially smaller number of training epochs than the BP algorithm, and in the case of the signal classification problem the MFT algorithm achieved much lower errors and misclassification rates.

原文English
頁面285-290
頁數6
出版狀態Published - 1992 12月 1
事件Proceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92 - St.Louis, MO, USA
持續時間: 1992 11月 151992 11月 18

Other

OtherProceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92
城市St.Louis, MO, USA
期間92-11-1592-11-18

All Science Journal Classification (ASJC) codes

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