Support Vector Machine

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Support Vector Machine

  1. 1. Support Vector Machine Putri W Novianti Victor L Jong Biostatistics & Research Support Julius Center for Health Sciences and Primary Care University Medical Center Utrecht
  2. 2. Support Vector Machine 2 • Binary classification method • The method finds the best decision hyperplane that separate sample from two classes with maximum margin
  3. 3. Support Vector Machine 3 [1]
  4. 4. Support Vector Machine 4 [1]
  5. 5. Support Vector Machine 5 [1]
  6. 6. Support Vector Machine 6 [1]
  7. 7. Support Vector Machine 7 [1]
  8. 8. Support Vector Machine 8 [1]
  9. 9. Support Vector Machine 9 [1]
  10. 10. Support Vector Machine 10 [1]
  11. 11. Support Vector Machine 11 What if the problem is not linearly separable? [2]
  12. 12. Support Vector Machine 12
  13. 13. Support Vector Machine 13 [1]
  14. 14. Support Vector Machine 14 [1]
  15. 15. Support Vector Machine 15 [1]
  16. 16. Support Vector Machine 16 [1]
  17. 17. Support Vector Machine 17 [4]
  18. 18. Support Vector Machine 18 [3]
  19. 19. Support Vector Machine 19 Multiclass outcome - SVM only handle binary classification - Although binary classification is the most common classification in microarray, multiclass outcome could be occur in practice - Modification is needed to handle multiclass outcome - one-versus-rest (OVR) - one-versus-one (OVO) [2]
  20. 20. Support Vector Machine 20 Multiclass outcome [2]
  21. 21. Support Vector Machine 21 OVR-SVM [2]
  22. 22. Support Vector Machine 22 OVO-SVM [2]
  23. 23. Support Vector Machine 23 [5]
  24. 24. Support Vector Machine 24 Example 1. Classification in Iris Data [5]
  25. 25. Support Vector Machine 25 Example 1. Classification in Iris Data [5]
  26. 26. Support Vector Machine 26 SVM for Regression
  27. 27. Support Vector Machine 27 SVM for Regression
  28. 28. Support Vector Machine 28
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  30. 30. Support Vector Machine 30
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  32. 32. Support Vector Machine 32 References [1] Zhang, X. Support Vector Machine. Lecture slides on Data Mining course. Fall 2010, KSA: KAUST [2] Statnikov, A. et al. 2005. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics, 21:5, 631-643 [3] Hastie, T., Tibshirani, R., Friedman, J. The elements of statistical learning, second edition. 2009. New York: Springer [4] Guyon, I et al. 2002. Gene selection for cancer classification using support vector machines. Machine Learning, 49, 389-422 [5] Meyer, D. et al. 2012. R package: e1071.

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