Classification of Magnetic Resonance brain images by using weighted radial basis function kernels

  title={Classification of Magnetic Resonance brain images by using weighted radial basis function kernels},
  author={Ching-Tsorng Tsai and Hsian Min Chen and J. W. Chai and Clayton Chi-Chang Chen and Chein-I Chang},
  journal={2011 International Conference on Electrical and Control Engineering},
The paper proposed a weighted Radial basis function kernel (WRBF) approach that can be used to detect and classify anomalies in Magnetic Resonance (MR) images. A weighted Radial basis function kernel (WRBF) approach, despite the fact that the idea of WRBF kernels can be traced back to the work [1], its application to Radial basis function (RBF) kernel is new. It includes the Support Vector Machines (SVMs) using RBF as its special case where the RBF is considered to be uniformly weighted… CONTINUE READING

From This Paper

Figures, tables, results, connections, and topics extracted from this paper.
3 Extracted Citations
7 Extracted References
Similar Papers

Referenced Papers

Publications referenced by this paper.
Showing 1-7 of 7 references

Weighted Radial Basis Function Kernels for Support Vector Machines Classification of Magnetic Resonance Brain Images

  • Shih-Yu Chen, Ching-Tsorng Tsai, Yen Chieh Ouyang, Jyh-Wen Chai, Clayton Chi-Chang Chen, Chein-I Chang
  • The 24th IPPR Conference on Computer Vision…
  • 2011
1 Excerpt

Independent Component Analysis for Magnetic Resonance Image Classification

  • Ouyang, Y.C, +5 authors S. K. Lee
  • EURASIP Journal on Advances in Signal Processing…
  • 2008
1 Excerpt

Similar Papers

Loading similar papers…