Minimum risk neural networks and weight decay technique

I. Cheng Yeh, Pei Yen Tseng, Kuan Chieh Huang, Yau Hwang Kuo

研究成果: Conference contribution

3 引文 斯高帕斯(Scopus)

摘要

To enhance the generalization of neural network model, we proposed a novel neural network, Minimum Risk Neural Networks (MRNN), whose principle is the combination of minimizing the sum of squares of error and maximizing the classification margin, based on the principle of structural risk minimization. Therefore, the objective function of MRNN is the combination of the sum of squared error and the sum of squares of the slopes of the classification function. Besides, we derived a more sophisticated formula similar to the traditional weight decay technique from the MRNN, establishing a more rigorous theoretical basis for the technique. This study employed several real application examples to test the MRNN. The results led to the following conclusions. (1) As long as the penalty coefficient was in the appropriate range, MRNN performed better than pure MLP. (2) MRNN may perform better in difficult classification problems than MLP using weight decay technique.

原文English
主出版物標題Emerging Intelligent Computing Technology and Applications - 8th International Conference, ICIC 2012, Proceedings
頁面10-16
頁數7
DOIs
出版狀態Published - 2012 八月 20
事件8th International Conference on Emerging Intelligent Computing Technology and Applications, ICIC 2012 - Huangshan, China
持續時間: 2012 七月 252012 七月 29

出版系列

名字Communications in Computer and Information Science
304 CCIS
ISSN(列印)1865-0929

Other

Other8th International Conference on Emerging Intelligent Computing Technology and Applications, ICIC 2012
國家China
城市Huangshan
期間12-07-2512-07-29

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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  • 引用此

    Yeh, I. C., Tseng, P. Y., Huang, K. C., & Kuo, Y. H. (2012). Minimum risk neural networks and weight decay technique. 於 Emerging Intelligent Computing Technology and Applications - 8th International Conference, ICIC 2012, Proceedings (頁 10-16). (Communications in Computer and Information Science; 卷 304 CCIS). https://doi.org/10.1007/978-3-642-31837-5_2