Effective Training of Support Vector Machines using Extractive Support Vector Algorithm

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 new design algorithm, called the extractive support vector algorithm, which improves learning speed 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 revealed that our proposed algorithm provides 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: median 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.