Support Vector Domain Classifier Based on Multiplicative Updates(Image/Visual Signal Processing)(<Special Section>Digital Signal Processing)

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概要

• 論文の詳細を見る

• This paper proposes a learning classifier based on Support Vector Domain Description (SVDD) for two-class problem. First, by the description of the training samples from one class, a sphere boundary containing these samples is obtained ; then, this boundary is used to classify the test samples. In addition, instead of the traditional quadratic programming, multiplicative updates is used to solve the Lagrange multiplier in optimizing the solution of the sphere boundary. The experiment on CBCL face database illustrates the effectiveness of this learning algorithm in comparison with Support Vector Machine (SVM) and Sequential Minimal Optimization (SMO).

• 社団法人電子情報通信学会の論文
• 2004-08-01

著者

• Zhang Taiyi

  The Xi'an Jiaotong University

• LU Congde

  the Xi'an Jiaotong University

• ZHANG Wei

  the Xi'an Jiaotong University

• Lu Congde

  The Xi'an Jiaotong University

• Zhang Wei

  The Xi'an Jiaotong University

関連論文

• Support Vector Domain Classifier Based on Multiplicative Updates(Image/Visual Signal Processing)(Digital Signal Processing)

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