An error-counting network for pattern classification

  • Authors:
  • Kar-Ann Toh

  • Affiliations:
  • Biometrics Engineering Research Center, School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea

  • Venue:
  • Neurocomputing
  • Year:
  • 2008

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Abstract

This paper presents a novel quadratic error-counting network for pattern classification. Two computational issues namely, the network learning issue and the classification error-counting issue have been addressed. Essentially, a linear series functional approximation to network structure and a smooth quadratic error-counting cost function were proposed to resolve these two computational issues within a single framework. Our analysis shows that the quadratic error-counting objective can be related to the least-squares-error objective by adjusting the class-specific normalization factors. The binary classification network is subsequently extended to cater for multicategory problems. An extensive empirical evaluation validates the usefulness of proposed method.