Support vector machine classifiers using RBF kernels with clustering-based centers and widths

@article{Daqi2007SupportVM,
  title={Support vector machine classifiers using RBF kernels with clustering-based centers and widths},
  author={Gao Daqi and Zhang Tao},
  journal={2007 International Joint Conference on Neural Networks},
  year={2007},
  pages={2971-2976}
}
This paper focuses on support vector machines (SVMs) with radial basis function (RBF) kernels to solve the large-scale classification problems. We decompose a large-scale learning problem into multiple two-class problems with the one-verse-all decomposition technique, and then propose an adoptively clustering method. An initial support vector (SV) coincides with a certain clustering center, and its width is equal to the max Euclid distance in the clustering region. Therefore, the initial number… CONTINUE READING

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