Eclectic Rule-Extraction from Support Vector Machines

@inproceedings{Barakat2004EclecticRF,
  title={Eclectic Rule-Extraction from Support Vector Machines},
  author={Nahla H. Barakat and Joachim Diederich},
  year={2004}
}
Support vector machines (SVMs) have shown superior performance compared to other machine learning techniques, especially in classification problems. Yet one limitation of SVMs is the lack of an explanation capability which is crucial in some applications, e.g. in the medical and security domains. In this paper, a novel approach for eclectic rule-extraction from support vector machines is presented. This approach utilizes the knowledge acquired by the SVM and represented in its support vectors… CONTINUE READING
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References

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Learning by Support Vector Machines from Huge Data Sets

  • V. Kecman
  • presented at KES 2004, Eighth international…
  • 2004
1 Excerpt

Machine learning with Neural Networks and support vector machines

  • H. Khuu, H. K. Lee, J-L, Tsai
  • University of Wisconsin,
  • 2004
1 Excerpt

C

  • H. Núñez
  • Angulo, and A.Catala, “Rule-extraction from…
  • 2002
1 Excerpt

Rule-extraction from Technology IPOs in the US Stock Market

  • R. Mitsdorffer, J. Diederich, C. Tan
  • presented at ICONIP02, Singapore
  • 2002
1 Excerpt

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