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COMP5331

COMP5331. Other Classification Models: Support Vector Machine (SVM). Prepared by Raymond Wong Presented by Raymond Wong raywong@cse. What we learnt for Classification. Decision Tree Bayesian Classifier Nearest Neighbor Classifier. Other Classification Models.

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COMP5331

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  1. COMP5331 Other Classification Models: Support Vector Machine (SVM) Prepared by Raymond Wong Presented by Raymond Wong raywong@cse

  2. What we learnt for Classification • Decision Tree • Bayesian Classifier • Nearest Neighbor Classifier

  3. Other Classification Models • Support Vector Machine (SVM) • Neural Network

  4. Support Vector Machine • Support Vector Machine (SVM) • Linear Support Vector Machine • Non-linear Support Vector Machine

  5. Support Vector Machine • Advantages: • Can be visualized • Accurate when the data is well partitioned

  6. Linear Support Vector Machine x2 w1x1 + w2x2 + b > 0 x1 w1x1 + w2x2 + b = 0 w1x1 + w2x2 + b < 0

  7. Linear Support Vector Machine

  8. Linear Support Vector Machine

  9. Linear Support Vector Machine

  10. Linear Support Vector Machine x2 Support Vector x1 Margin We want to maximize the margin Why?

  11. Linear Support Vector Machine x2 w1x1 + w2x2 + b - D = 0 x1 w1x1 + w2x2 + b = 0 w1x1 + w2x2 + b + D = 0

  12. Linear Support Vector Machine Let y be the label of a point x2 +1 +1 w1x1 + w2x2 + b - 1  0 +1 +1 -1 w1x1 + w2x2 + b - 1 = 0 -1 -1 -1 x1 w1x1 + w2x2 + b + 1  0 w1x1 + w2x2 + b = 0 w1x1 + w2x2 + b + 1 = 0

  13. Linear Support Vector Machine Let y be the label of a point y(w1x1 + w2x2 + b)  1 x2 +1 +1 w1x1 + w2x2 + b - 1  0 +1 +1 -1 w1x1 + w2x2 + b - 1 = 0 -1 -1 y(w1x1 + w2x2 + b)  1 -1 x1 w1x1 + w2x2 + b + 1  0 w1x1 + w2x2 + b = 0 w1x1 + w2x2 + b + 1 = 0

  14. Margin |(b+1) – (b-1)| = 2 = Linear Support Vector Machine Let y be the label of a point y(w1x1 + w2x2 + b)  1 x2 +1 +1 +1 +1 -1 w1x1 + w2x2 + b - 1 = 0 -1 -1 y(w1x1 + w2x2 + b)  1 -1 x1 Margin w1x1 + w2x2 + b + 1 = 0 We want to maximize the margin

  15. 2 = Linear Support Vector Machine • Maximize • Subject to • for each data point (x1, x2, y)where y is the label of the point (+1/-1) Margin y(w1x1 + w2x2 + b)  1

  16. Linear Support Vector Machine • Minimize • Subject to • for each data point (x1, x2, y)where y is the label of the point (+1/-1) 2 y(w1x1 + w2x2 + b)  1

  17. Linear Support Vector Machine • Minimize • Subject to • for each data point (x1, x2, y)where y is the label of the point (+1/-1) Quadratic objective Linear constraints y(w1x1 + w2x2 + b)  1 Quadratic programming

  18. Linear Support Vector Machine • We have just described 2-dimensional space • We can divide the space into two parts by a line • For n-dimensional space where n >=2, • We use a hyperplane to divide the space into two parts

  19. Support Vector Machine • Support Vector Machine (SVM) • Linear Support Vector Machine • Non-linear Support Vector Machine

  20. Non-linear Support Vector Machine x2 x1

  21. Non-linear Support Vector Machine • Two Steps • Step 1: Transform the data into a higher dimensional space using a “nonlinear” mapping • Step 2: Use the Linear Support Vector Machine in this high-dimensional space

  22. Non-linear Support Vector Machine x2 x1

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