Affiliations: Department of Computer Science and Information
Engineering, Chaoyang University of Technology, Wufong, Taichung 41349, Taiwan.
[email protected] | Department of Computer Science and Information
Engineering National Chung Cheng University, Taiwan | Department of Computer Science and Information
Engineering, Chaoyang University of Technology
Note: [] Address for correspondence: Department of Computer Science and
Information Engineering, Chaoyang University of Technology
Abstract: The major problem of SVMs is the dependence of the nonlinear
separating surface on the entire dataset which creates unwieldy storage
problems. This paper proposes a novel design algorithm, called the extractive
support vector algorithm, which provides improved learning speed and a vastly
improved performance. Instead of learning and training with all input patterns,
the proposed algorithm selects support vectors from the input patterns and uses
these support vectors as the training patterns. Experimental results reveal
that our proposed algorithmprovides near optimal solutions and outperforms the
existing design algorithms. In addition, a significant framework which is based
on extractive support vector algorithm is proposed for image restoration. In
the framework, input patterns are classified by three filters: weighted order
statistics filter, alpha-trimmed mean filter and identity filter. Our proposed
filter can achieve three objectives: noise attenuation, chromaticity retention,
and preservation of edges and details. Extensive simulation results illustrate
that our proposed filter not only achieves these three objectives but also
possesses robust and adaptive capabilities, and outperforms other proposed
filtering techniques.
Keywords: support vector machines, unwieldy storage, image restoration, weighted order statistics filter, alpha-trimmed mean filter
