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Minimum Risk Neural Networks and Weight Decay Technique

  • I-Cheng Yeh
  • Pei-Yen Tseng
  • Kuan-Chieh Huang
  • Yau-Hwang Kuo
Part of the Communications in Computer and Information Science book series (CCIS, volume 304)

Abstract

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.

Keywords

multi-layer perceptrons weight decay support vector machine structural risk minimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • I-Cheng Yeh
    • 1
  • Pei-Yen Tseng
    • 2
  • Kuan-Chieh Huang
    • 3
  • Yau-Hwang Kuo
    • 3
  1. 1.Department of Civil EngineeringTamkang UniversityTaiwan
  2. 2.Department of Information ManagementChung Hua UniversityTaiwan
  3. 3.Department of Computer Science and Information EngineeringNational Cheng Kung UniversityTaiwan

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