### Abstract

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.

Original language | English |
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Pages | 285-290 |

Number of pages | 6 |

Publication status | Published - 1992 Dec 1 |

Event | Proceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92 - St.Louis, MO, USA Duration: 1992 Nov 15 → 1992 Nov 18 |

### Other

Other | Proceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92 |
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City | St.Louis, MO, USA |

Period | 92-11-15 → 92-11-18 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Software

### Cite this

*Training neural pattern classifiers with a mean field theory learning algorithm*. 285-290. Paper presented at Proceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92, St.Louis, MO, USA, .

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**Training neural pattern classifiers with a mean field theory learning algorithm.** / Chiang, Jung-Hsien; Pimmel, Russell.

Research output: Contribution to conference › Paper

TY - CONF

T1 - Training neural pattern classifiers with a mean field theory learning algorithm

AU - Chiang, Jung-Hsien

AU - Pimmel, Russell

PY - 1992/12/1

Y1 - 1992/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0027105487&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0027105487&partnerID=8YFLogxK

M3 - Paper

SP - 285

EP - 290

ER -