240 likes | 440 Views
Outline of Talk. Support Vector Machine (SVM) Classifiers. Standard Quadratic Programming formulation. The DNA promoter dataset. Polyhedral Knowledge Sets. Knowledge-Based SVMs. Empirical Evaluation. Conclusion. Wisconsin breast cancer prognosis dataset. . Incorporating knowledge sets i
E N D
1. Knowledge-Based Support Vector Machine Classifiers Glenn Fung
Olvi Mangasarian
Jude Shavlik
2. Outline of Talk
3. Support Vector MachinesMaximizing the Margin between Bounding Planes
4. Support Vector MachinesMaximizing the Margin between Bounding Planes
5. Algebra of the Classification Problem 2-Category Linearly Separable Case
6. Support Vector Machines Quadratic Programming Formulation
7. Support Vector MachinesLinear Programming Formulation
8. Knowledge-Based SVM via Polyhedral Knowledge Sets
9. Incorporating Knowledge Sets Into an SVM Classifier
10. Knowledge Set Equivalence Theorem
11. Proof of Equivalence Theorem( Via Nonhomogeneous Farkas or LP Duality)
12. Knowledge-Based SVM Classification
13. Knowledge-Based SVM Classification
14. Knowledge-Based LP with Slack VariablesMinimize Error in Knowledge Set Constraints Satisfaction
15. Knowledge-Based SVM via Polyhedral Knowledge Sets
16. Empirical EvaluationThe Promoter Recognition Dataset
17. The Promoter Recognition DatasetNumerical Representation
18. Promoter Recognition Dataset Prior Knowledge Rules
19. Promoter Recognition Dataset Sample Rules
20. The Promoter Recognition DatasetComparative Test Results
21. Wisconsin Breast Cancer Prognosis Dataset Description of the data
22. Wisconsin Breast Cancer Prognosis Dataset Numerical Testing Results
23. Conclusion
24. Future Research
25. Web Pages