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Support Vector Domain Classifier Based on Multiplicative Updates
Congde LU Taiyi ZHANG Wei ZHANG
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2004/08/01
Print ISSN: 0916-8508
Type of Manuscript: Special Section LETTER (Special Section on Digital Signal Processing)
Category: Image/Visual Signal Processing
support vector machine, support vector domain description, sequential minimal optimization, multiplicative updates,
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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).