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  1. ICML-99 (A. Aamodt)

  2. ICML-99 (A. Aamodt)

  3. ICML-99 (A. Aamodt)

  4. excerpt ICML-99 (A. Aamodt)

  5. ICML-99 (A. Aamodt)

  6. CBR methods The Data-- Knowledge Dimension • Data intensive - Knowledge poor • - A case is a data record - Similarity asessment based on simple metric • Knowledge intensive - Data Poor - A case is a user experience - Similarity asessment is an explanation process • Both knowledge and data intensive - Multiple case contents - Multiple similarity asessment methods ICML-99 (A. Aamodt)

  7. ICML-99 (A. Aamodt)

  8. Dynamic Memory (Scank & Kolodner 83) ICML-99 (A. Aamodt)

  9. Example ICML-99 (A. Aamodt)

  10. Category Structure (Porter & Bareiss 87) ICML-99 (A. Aamodt)

  11. ICML-99 (A. Aamodt)

  12. ICML-99 (A. Aamodt)

  13. ICML-99 (A. Aamodt)

  14. ICML-99 (A. Aamodt)

  15. ICML-99 (A. Aamodt)

  16. ICML-99 (A. Aamodt)

  17. ICML-99 (A. Aamodt)

  18. ICML-99 (A. Aamodt)

  19. ICML-99 (A. Aamodt)

  20. ICML-99 (A. Aamodt)

  21. ICML-99 (A. Aamodt)

  22. ICML-99 (A. Aamodt)

  23. t h i n g g e n e r i c c o n c e p t s g d o m a i n c o n c e p t s c a s e s c a s e c a s e c a s e 0 3 9 7 6 1 1 2 CreekL Knowledge Types l e n e r a ICML-99 (A. Aamodt)

  24. ICML-99 (A. Aamodt)

  25. ICML-99 (A. Aamodt)

  26. Integrated approaches • Case-based and inductive learning - CBR & Data Mining • CBR and decision trees - Example: INRECA (Esprit III) • CBR and Bayesian networks - Example: NOEMIE (Esprit IV) ICML-99 (A. Aamodt)